IN DEGREE PROJECT THE BUILT ENVIRONMENT,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2020

Modelling Layover Parking Capacity in Bus TerminalsA Case Study of Stockholm

JERKER NYBLIN

MOHAMMAD AL-MOUSA

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT

Modelling Layover Parking Capacity in Bus Terminals A Case Study of Stockholm

JERKER NYBLIN MOHAMMAD AL-MOUSA

Degree Project in The Built Environment, Second Cycle, 30 Credits KTH Royal Institute of Technology Stockholm, Sweden 2020

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I. ABSTRACT

Public bus services play a pivotal role in urban public transport systems, they represent the flexible fragment of the mass transit chain in Stockholm county. It extends the high-capacity rail services reach by creating local connections to rail stations, by creating connections between local centres and by creating direct connections during peak hours to relieve the rail system. As Stockholm continues to grow, there is an increased demand on bus services and its facilities which has created challenges in providing suitably dimensioned bus terminals. Even though public transport operators have been using computerised solutions to optimise their operational schedule for vehicles and employees, the increased intensity of public bus services has resulted in an increased demand for layover parking capacity in bus terminals.

Several bus terminals in the Stockholm region are reaching their maximum capacity. Layover parking capacity issues are indirectly causing bus service delays and municipalities have strong interests in minimising the land use of bus terminals since they occupy attractive land near other public transport services such as underground and over-ground train services. The layover parking capacity issue leads to increasing operational cost, increasing environmentally unsound deadheading, and decreasing service resilience against service abnormalities.

The Public Transport Administration (Trafikförvaltningen) of the Region Stockholm recognises the need to have efficient land use for terminals. One of the most important aspects of a bus terminal’s land use is dimensioning the bus layover parking facility. The proper design and the sufficient dimensioning of a bus layover parking facility is an essential need for operators’ traffic planners to optimise their vehicle and crew schedules in order to provide a reliable, punctual, efficient and environmentally friendly service with minimal operationally inefficient deadheading between terminals.

In this master’s thesis, a study was carried out on bus terminals in Stockholm county. The study included literature review, field studies and a survey targeting public bus operators. The aim of the study was to create a model that estimates the optimum bus layover parking capacity at early planning stages for efficient bus operations in different bus terminals in Stockholm county.

The resulting model was derived by means of regression analysis from a sample of bus terminals identified according to the findings of the survey. The study found several bus service and bus terminal attributes that act as predictors for the optimum layover parking capacity, such as service frequency in the terminal and trip durations. In addition to what the guidelines of Transit Capacity and Quality of Service Manual 2013 suggest, this study considered the effect of scheduled connections between buses and the commuter trains, without the need of providing detailed information of the schedules of bus services.

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II. SAMMANFATTNING

Busstrafiken spelar en viktig roll i dagens kollektivtrafiksystem och i Stockholm utgör busstrafiken den flexibla delen av kollektivtrafikutbudet. Busstrafiken utökar de kapacitetsstarka spårbundna färdmedlens täckningsområde genom att erbjuda kundnära anslutningstrafik till och från spårtrafiksstationer, skapar förbindelser mellan lokala centrum och avlastar spårtrafiken genom att skapa direkta förbindelser under rusningstid. Efterfrågan på busstrafik och dess infrastruktur ökar i den alltjämt växande stockholmsregionen och detta skapar utmaningar i att tillhandahålla välanpassad bussterminalkapacitet. Busstrafikutövarna använder datoriserade trafik-optimeringsverktyg för att optimera nyttjandet av fordon och personal men behovet av bussuppställningsplatser ökar alltjämt med ett växande busstrafikutbud.

Nyttjandet i flera bussterminaler i stockholmsregionen närmar sig sin maxkapacitet. Bussuppställningsplatsbrist leder indirekt till förseningar och kommuner har starka incitament för att minimera bussterminalernas storlek, detta då de ofta upptar värdefull mark i spårtrafiknära lägen. Brist på bussuppställningsplatskapacitet leder till höjda operativa kostnader, fler miljöskadliga tomkörningar och försämrad motståndskraft mot trafikproduktionsavvikelser.

Trafikförvaltningen i Region Stockholm är av åsikten att bussterminalers mark-användning måste vara effektiv och en av de viktigaste aspekterna av bussterminalers markanspråk är ytan för bussuppställning. Välutformade bussterminaler med adekvat bussuppställningsplatskapacitet är däremot ett krav för att trafikutövarna ska kunna köra pålitlig, punktlig, effektiv busstrafik med lägsta miljöpåverkan och med minimalt antal tomkörningar som inte är motiverade för att höja trafikproduktions-effektiviteten.

I denna masteruppsats redogörs för en studie som gjordes av bussterminalerna i Stockholms län. I studien ingick litteraturanalys, fältstudier och en enkät-undersökning. Målet med studien var att skapa en modell för skattning av bussuppställningsplatskapacitetsoptimum som kan användas tidigt i planerings-arbetet för ny- eller ombyggnation av bussterminaler i Stockholms län.

Med hjälp av regressionsanalys utvecklades en modell utifrån resultatet från enkätundersökningen och studien fann flera busstrafikerings- och bussterminal-attribut som tjänar som förklaringsvariabler för bussuppställningsplatskapacitets-optimum, såsom turtäthet och körtider. I tillägg till riktlinjerna som föreslås av Transit Capacity and Quality of Service Manual 2013 tar denna studie även hänsyn till frekvensen hos tidspassade anslutningar mot annan kollektivtrafik men utan att kräva detaljerad tidtabellsinformation.

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III. ACKNOWLEDGEMENTS

Special thanks to Associate Professor Albania Nissan, Ph.D. Azhar Al-Mudhaffar and Kenneth Domeij for believing in us and for supervising our project.

Thanks to Mark Kesper at Trafikförvaltningen for going great lengths in taking care and effort in assisting us in our work. Furthermore, we would like to thank everyone at the Public Transport Administration of Region Stockholm who has assisted us in our work for taking time for our questions and for their valuable input. We would also like to thank Charlotte Söderberg and Johan Wahlstedt at Ramboll for their engagement and interesting discussions.

Thanks to Arriva, Keolis, Nobina and Transdev for their valuable input.

The authors would also like to thank their respective families for support, patience and love during the project. It would not have been possible to make this thesis without your wholehearted support.

This master’s thesis has been produced during Mr. Mohammad Al-Mousa’s scholarship period at KTH Royal Institute of Technology, funded by the Swedish Institute.

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CONTENTS I. ABSTRACT ..................................................................................... II

II. SAMMANFATTNING ..................................................................... III

III. ACKNOWLEDGEMENTS .............................................................. IV

IV. DEFINITIONS ................................................................................ VII

1. INTRODUCTION ............................................................................. 1 1.1. Background ............................................................................................. 2

1.2. Scope and Delimitations......................................................................... 4

1.3. Outline ..................................................................................................... 5

2. LITERATURE REVIEW ................................................................... 6 2.1. Intermodal Travel .................................................................................... 6

2.2. Reliability of Bus Services ...................................................................... 7

2.3. Design of Bus Terminals ...................................................................... 10 2.3.1. Classifications of Bus Terminals ................................................... 11

2.4. Capacity of Bus Terminals ................................................................... 16 2.4.1. Capacity of Bus Stops in Bus Terminals....................................... 17 2.4.2. Capacity of Bus Layover Parking .................................................. 20

2.5. Reflection on the literature ................................................................... 24

3. CASE STUDY OF STOCKHOLM ...................................................25 3.1. Public Bus Network .............................................................................. 25

3.1.1. Contract Areas ............................................................................... 25 3.1.2. Bus Lines and Bus Terminals ....................................................... 27 3.1.3. Interlining ........................................................................................ 28

3.2. Bus Layover Parking in Bus Terminals ................................................ 32 3.2.1. Traffic Movements in Bus Terminals............................................. 32 3.2.2. Layover Activities and Amenities .................................................. 33 3.2.3. Assessment of Bus Terminals ....................................................... 35

3.3. Demand for Layover Parking ............................................................... 36 3.3.1. Service Variables ........................................................................... 38 3.3.2. Network Attributes ......................................................................... 41

3.4. Practical Adaptation Techniques ......................................................... 43

3.5. Available Resources and Limitations ................................................... 47

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4. METHODOLOGY ...........................................................................49 4.1. Data Collection ..................................................................................... 49

4.1.1. The Survey ..................................................................................... 49 4.1.2. On-site Research ........................................................................... 54 4.1.3. Timetables and Scheduled Bus Departures ................................. 54 4.1.4. Google Cloud and The Distance Matrix ........................................ 54

4.2. Data Analysis ........................................................................................ 55 4.2.1. Operational variables and regression analysis............................. 55 4.2.2. Network Analysis ........................................................................... 57

5. RESULTS.......................................................................................65 5.1. The Questionnaire ................................................................................ 65

5.1.1. Driver Pause and Driver Break Facilities ...................................... 65 5.1.2. Level of Service ............................................................................. 66

5.2. Eliminated Variables ............................................................................. 68 5.2.1. Facility Variables ............................................................................ 68 5.2.2. Centrality Variables........................................................................ 70 5.2.3. Eliminated Bus Terminals .............................................................. 71

5.3. The Model ............................................................................................. 71 5.3.1. Moderation Analysis ...................................................................... 71 5.3.2. Variables Selection ........................................................................ 73 5.3.3. Statistical Testing ........................................................................... 79 5.3.4. Model Validation ............................................................................ 82

6. DISCUSSION .................................................................................85 6.1. Comments on Bus Terminals ............................................................... 86

6.2. Comparison ........................................................................................... 89

6.3. Work Limitations ................................................................................... 93

7. CONCLUSION ...............................................................................98 7.1. Future Research ................................................................................. 100

8. REFERENCES ............................................................................. 101

9. APPENDICES .............................................................................. 105 APPENDIX A ................................................................................................. 106

APPENDIX B ................................................................................................. 108

APPENDIX C ................................................................................................ 113

APPENDIX D ................................................................................................ 115

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IV. DEFINITIONS

To make full use of the information in this report it is important to understand certain keywords and abbreviations that are not commonly used in everyday language. The terminology may not be equal to Standard English.

Abbreviations

Abbreviation Definition AVL Automatic Vehicle Location

BBA Bussbranschavtalet, the sector-wide agreement on the working conditions for bus drivers between Bussarbetsgivarna and Kommunalarbetareförbundet.

BRT Bus Rapid Transport, high service bus lines, comparable to service levels traditionally offered by rail

HCM Highway Capacity Manual of the American Transportation Research Board

PKM Passenger Kilometre is a measure of movement of passengers by a certain mode of travel that is equal to number of passengers multiplied by the total travelled distance.

TCQSM Transit Capacity and Quality of Service Manual of the American Transportation Research Board

TSP Transit priority signal

Terminology

English term Swedish translation Description

Accessibility Tillgänglighet

A measure of the ability of the transport system to provide individuals with access to land-use, usually a space with activities or a destination of interest

Bus platoons Busskolonn A group of two or more buses moving together as a convoy, mainly due to lack of on-time performance.

Bus stop Busshållplats Where a bus stops for loading and/or disembarkation of passengers. A bus terminal can contain several bus stops.

Bus terminal Bussterminal Bus interchange where passengers can transfer between lines and modes. Further defined in chapter two

Bus terminal capacity Bussterminalkapacitet

The maximum number of buses that can be served by a bus terminal during a period of time with a given level of reliability.

Deadheading Tomkörning The practice of letting the bus driver drive the bus empty between two points of the bus service network

Driver break (Förar)rast Break of a minimum of thirty minutes in compliance with the terms set in the BBA.

Driver pause (Förar)paus Pause of a minimum of ten minutes in compliance with the terms set in the BBA.

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English term Swedish translation Description

Feeder lines Matarlinje A public transport line which role in the network is to provide local connectivity for main/trunk lines.

Giro HASTUS Giro HASTUS Software to optimise crew and fleet planning, market leader in Stockholm

IBM SPSS Statistics IBM SPSS Statistics A software for statistical analysis currently

owned and developed by IBM Corporation. Layover parking berth Uppställningsplats A place where an inactive bus vehicle can

be parked Layover parking zone Bussuppställningsområde A facility or an area that contains one or

more layover parking berths or equivalent. On-time performance Punktlighet Punctuality, adherence to the timetable

Samtrafiken Samtrafiken An umbrella organisation for actors in the public transport sector of Sweden

Short turning Avkortad linje Turning the bus in the opposite direction at a point on the route other than the terminal.

Slack time Slacktid Unproductive time in a bus block.

Stop-skipping Genomgångs-

The practice of having a public transport vehicle pass an existing stop without stopping for the purpose of saving travel time.

The Public Transport Administration (in Region Stockholm)

Trafikförvaltningen

The authority responsible for coordination of public transport in the Stockholm region and owner of Storstockholms Lokaltrafik brand.

Vehicle block Fordonsomlopp A set order of tasks/trips assigned to a vehicle

Vehicle holding Tidsreglering längs linje Stopping the bus at a certain stop if the bus precedes its schedule.

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1. INTRODUCTION

Bus services play a pivotal role in urban public transport systems, they are often recognised to accommodate the larger share of public transport users (Pattnaik, Mohan, & Tom, 1998). In Stockholm county, the trunk bus lines (“the blue buses”) alone undertook 250,000 journeys on a daily average in 2012, which is comparable with all the commuter train services of Stockholm. For all bus services in the county ridership is almost equal to the metro service in Stockholm county with around one million journeys per day (The City of Stockholm Traffic Administration, 2012). On a normal winter weekday between 6 am and 9 am in 2017, the overall bus services in Stockholm county performed around 1,800,000 passenger-kilometre (PKM), a value that is larger than both commuter and metro trains each (AB Storstockholms Lokaltrafik, 2017). The attractiveness of bus services comes from the fact that they cover large areas and operate on regular road networks, since they offer more cost-efficient accessibility for shorter journeys in comparison with other urban transport modes that provide more reliable and higher capacity services for longer distances such as the metro system (Button & Rietveld, 1999).

Although decision makers in many European cities argue that travellers have a preference for rail-based modes of travel over buses (Varelaa, Börjesson, & Dalyc, 2018), accessibility indicators for those modes are yet hard to improve, they demand large construction costs and relatively high urban densities that would grant sufficient revenues to make those investments financially feasible (Ingvardson, 2017). Bus services on the other hand, are the flexible fragment of today’s mass transit chain, they offer space for route alterations and improvements. Whether to serve direct links to the railway system through feeder lines, or to relieve railway systems through BRT, bus services have established their role as the backbone in modern urban mobility, which Cheng and Tseng (2016) described as “the nucleus of urban rail transit services”.

The Public Transport Administration of the Stockholm Region got a mission to increase the public transport share of all travel in Stockholm County to fifty-four percent by 2030 (Stockholms läns landsting, 2017). Several hubs are due for reconstruction before 2030: Haningeterrassen, Södertälje station and Brommaplan, among others. Two of the goals for The Public Transport Administration in Stockholm County are for the public transport services to be “competitive” and “smart”. To reach the competitive goal the customers need to be satisfied, and to reach the smart goal the system needs to be efficient. The fulfilment of the 2030 goals would lead to increased demands on already congested bus terminals, which must be able to contain an increased number of services without a decrease in on-time-performance and without an increase in the inefficient practice of deadheading.

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1.1. Background

The continuous growth of cities comes with an increasing demand for bus services, where the population growth can be as large as twenty-five percent until 2030 for cities like Stockholm. Considering this fast growth, together with the efforts of promoting sustainable urban transport and the significance of the role of bus services in this promotion, the resources needed to operate an effective, sustainable, and comfortable bus services will naturally increase (The City of Stockholm Traffic Administration, 2012). These resources can include for example: capital investments in early planning stages, operational expenditures, overhead costs, etcetera. Among these resources, arises a conflict between two aspects of the service, those aspects are service quality and the infrastructural spaces dedicated for bus services.

A well-known example of this conflict is the exclusive bus lanes in dense urban centres. Exclusive bus lanes provide an increase in bus service speeds, effectiveness and capacity on one hand, on the other hand, they require dedicated spaces in corridors that can be part of attractive and dense urban areas like the ones in a city centre (Arasan & Vedagiri, 2010; Abdelfatah & Abdulwahid, 2017), this makes it difficult for policy makers to enforce dedicated bus lanes at the expense of other road or space users, even though it upgrades the quality of bus services.

In many bus terminals, a special parking zone is dedicated for buses when they are between timely separated departures, where the bus driver may take a break or for buses when they are waiting for the arrival of train services to provide a connection for passengers, this zone is referred to as a bus layover parking zone in bus terminals. Similar to exclusive bus lanes, bus layover parking zones usually require dedicating a share of the limited and attractive land for bus operations purposes. Municipal bodies and common citizens have a preference for reducing the footprint of bus infrastructure in attractive locations to enable other developments leading to higher real estate capitalisation and more vibrant places. On the other hand, public transport agencies in general, and public transport authorities in specific, prefer to dedicate these spaces for bus operations in order to optimise bus operations for better service reliability, decreased environmental impact and lower operational cost.

Before the introduction of optimisation tools for bus schedules in the beginnings of the 1960’s (Lloret Cendales, 2019), the demand for resources to run a certain bus service was larger than today. Capacity issues back then were important in the negotiations between stakeholders when initiating transport investments, and attention was given to finding solutions with proper capacity. As these computerised optimisation tools started to surface and became the more common approach to plan bus services, the resources needed to run a certain bus service became gradually smaller as the optimisation tools improved and the already-established infrastructure continued to provide enough capacity to run bus services according to the desired quality. Therefore, capacity issues in bus terminals were considered a non-issue for many years whereas bus service volumes continued to grow.

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This thesis deals with the new clash of interests that has started to arise in recent years since the opportunity for further optimisation of operations grows smaller while bus service volumes continue to grow. The issue is how much space should be devoted for buses when they are out-of-service or laying over in a bus terminal, and attention towards capacity issues in bus terminals have become indispensable again.

This conflict of interest over space usually creates lengthy negotiations between public transport authorities and others such as urban designers, municipalities, and real estate developers on how much space should be dedicated for layover parking activities for buses in terminals. During these negotiations, real estate developers can often present strong cases for their development plans, presenting municipalities with attractive offers for development of locations that are perceived as unattractive from an average citizen’s point of view, while public transport authorities often find themselves wanting in credible arguments defending their interest in securing adequate space for efficient bus operations. Hence, this kind of negotiations usually leads to an outcome that is unfavourable for bus operations. To improve the public transport authorities’ cases in this kind of negotiations a tool for better estimation of layover parking capacity is needed.

Insufficiency in the layover parking capacity has a direct impact on the service quality and on the operational costs that public transport operators have to endure. When a bus terminal is planned to have a certain capacity for its bus layover parking zone, the adequacy of the provided capacity cannot be confirmed until the bus terminal is operational, and then it is too late to make the required changes. This mismatch between planning and operations will result in buses deadheading from one location to another in order to find a place with enough space for layover activities. Induced deadheading due to layover parking capacity constraints increases the risk of operational irregularities, such as missed connections and decreased overall punctuality while increasing operational cost and complexity. In the longer perspective this leads to higher prices, more pollution, and less satisfied customers.

If the bus terminal has insufficient layover parking capacity for a certain bus service intensity, public bus operators will need to incorporate further planning techniques that can help the service to preserve a better quality, such as deadheading, but these incorporations come with extra cost for the operations, and the enhancement in service quality is not guaranteed. Also, due to limited layover parking capacity in bus terminals, and as public bus operators try to adopt to such limitations, the impact of the service on the environment will increase. Increased deadheading means more energy consumption, more emissions of air pollutants and lower air quality.

This master’s thesis report, developed in collaboration with the Public Transport Administration (Trafikförvaltningen) in Stockholm region, recognises the challenges of promoting sustainable urban mobility, and the need to have a strategic tool that can be used in the planning stages to predict future layover parking capacity requirement, to secure efficient bus operations for a growing region while not unneccesarily curbing urban development.

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1.2. Scope and Delimitations

The main goal of this thesis is to develop a strategic tool that can be used by The Public Transport Administration in Stockholm county (Trafikförvaltningen) to have an adequate estimation of the optimum capacity of layover parking zones in bus terminals, which can form a basis for the negotiations of land acquisitions for bus terminals between The Public Transport Administration in Stockholm county (Trafikförvaltningen) and other municipal bodies during the planning stages of bus terminals. To establish this goal, two research objectives have been formed:

x To analyse current bus operations

x To assess current bus terminals’ planning, and its interaction with bus operations (supply – demand interaction)

To be able to create such a tool and to accomplish the goal, a series of research questions are covered in this report:

x For what purpose is the layover parking used today?

x How do timed connections impact the need for layover parking capacity?

x How does the design of bus layover parking effect a bus terminal’s capacity?

x Is it possible to use bus stops as layover parking?

x How would the use of bus stops as layover parking impact service production?

x What defines a shortage of layover parking?

x How does a shortage of layover parking impact cost efficiency and service punctuality?

x How will service development impact the requirement for layover parking?

x What correlations exist between service volume (as in the number of departures on all lines), number of bus stops for departing buses and layover parking?

x Which other factors impact the requirements for layover parking?

The creation of a model that can describe the optimum layover parking capacity for bus terminals should be bounded by an input for the model that is known for traffic planners during planning stages. For example, detailed bus timetables are details that are on a tactical level which are not known during planning stages, thus, the model should not have detailed timetable information as an input to make predictions about layover parking requirements for buses. This thesis report does not cover topics that are related to safety aspects of the bus terminal, nor sustainability unless when it is related to impacts of limited layover parking on bus operations. The thesis does not cover aspects of bus terminal design related to non-vehicular facilities.

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1.3. Outline

This thesis starts with a literature study in chapter two which reviews previous studies that focuses on two aspects: bus service quality and its role in intermodal travel, and design and planning of bus terminals that covers capacity limitations. Chapter three is a case study for the bus terminals in Stockholm region, and how their layover parking capacity limitations effect bus scheduling; in this chapter, a discussion on how public transport operators adopt to these limitations are presented along with candidate factors that can be used to identify those limitations. Data collection and analysis is provided in chapter four, whereas the results of this study are provided in chapter five. Reflections on data are provided in each chapter with a longer discussion in chapter six. Conclusions are found in chapter seven.

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2. LITERATURE REVIEW

This chapter present a review of literatures that focus on the role of buses in urban travel, the importance of efficient connections between buses and other modes of transport, reliability of bus services and the factors influencing it, different designs of bus terminals and their classifications, and finally, the capacity of bus terminals.

2.1. Intermodal Travel

Bus terminals represent a rallying point for travellers intending to use buses as at least one of the modes of transport in their journey, making the bus service more cost-efficient by increasing the number of passengers onboard of each bus departure. Some larger bus terminals provide an opportunity for travellers to change their mode of transport within the same facility such as to or from trams and underground trains. The concept of using several modes of transport for the same journey is referred to as intermodality. Intermodality aims to promote sustainable mobility by creating travel conditions that have a minimum overall negative impact that is produced from the combination of the travel modes of a certain journey (Pitsiava-Latinopoulou & Iordanopoulos, 2012).

Pitsiava-Latinopoulou & Iordanopoulos (2012) suggested six effectiveness measures that ensure efficient transfers between the combinations of different routes and modes of travel within an integrated intermodal terminal, where the first measure is that the terminal should have a reliable and adequate level of service for all of the travel modes that are offered in that terminal. This measure points out that the existence of any unreliable travel service offered in a specific terminal can negatively impact the connectivity between the different modes and ultimately the travellers’ perception of the overall multimodal travel experience. The second effectiveness measure that is in point of interest is that intermodality should offer a reduced travel time when compared with a similar unimodal trip. The latter measure puts further emphasise on the requirement of having reliable modes of travel to establish timetables that can ensure synchronised connections that offer reduced travel times.

Pitsiava-Latinopoulou & Iordanopoulos (2012) have conducted a case study on the intermodal connectivity of thirty-two major transit areas in Athens using quantitative results of a survey combined with field observations. Their study has concluded that the number of transport services that are offered in a multimodal terminal alone does not result in an effective intermodality, but intermodality effectiveness comes through establishing harmonised services and that the different modes’ platforms should not be largely distant from each other as well. Several other literatures were confirmative to Pitsiava-Latinopoulou & Iordanopoulos (2012) findings, and they argue that the existence of uncomfortable or inconvenient intermodal transfers can jeopardise the whole public transport market competitiveness against private cars; it can affect both current and potential public transport users negatively (Cheng & Tseng, 2016; Wardman, Hine, & Stradling, 2001; Hine & Scott, 2000).

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Acknowledging those findings, and as decision makers in most European cities aim to reduce car dependency, today’s modern transport policies have shown explicit need to increase the attractiveness of public transport through creating a service that complements the needs of public transport users, whether through improvements in service reliability, service information, safety or comfort (Hine & Scott, 2000), and extra attention has been shed over the integration of public transport modes (Secretary of State for Scotland, 1998; DETR, 1998) , where the City of Stockholm has given the greater priority to ensuring efficient transfers between public transport modes in their action plan for the period 2012-2016 (The City of Stockholm Traffic Administration, 2012).

Although modern transport policies aim to promote active transport modes such as walking, cycling and the recently introduced electric scooters as part of intermodal travel chain due to their positive impacts on health, environment and land use (Millward, Spinney, & Scott, 2013), yet, bus services will still be the backbone for intermodal travel for several reasons. Opposing to what active transport offers, bus services can accommodate all users with physical limitations, also, connections that are established between feeder buses and rail lines are irreplaceable solution for providing access to the public transport system in urban areas with high demand for mass transit (Kuan, Ong, & Ng, 2006), this particular measure is a fundamental component in accessibility measures (Djurhuus, Hansen, Aadahl, & Glümer, 2016) that is clearly manifesting in the ridership of coordinated and reliable feeder systems.

2.2. Reliability of Bus Services

Although bus services operate on conventional roads, enabling their operations to expand geographically in a cost-efficient manner compared to rail-based modes, this particular set exposes buses to further traffic friction with other vehicles in the road network, and this exposure increases the unpredictability in bus travel times. Service reliability is a principal element in service quality (The European Committee for Standardization, 2002), it can be defined as the ability of a service-provider to adhere to the agreed-upon service in terms of several aspects such as: bus loading capacity, service comfort, vehicles condition, service frequency and adherence to the planned time tables (Ceder, 2007; Chen, Yu, Zhang, & Guo, 2009; Kimpel, 2001; Oort, 2011).

Punctuality and regularity of bus services are two of the main measures of service reliability, where punctuality reflects quantitively the adherence of the provided service to the planned departure or arrival times (Yaakub & Napiah, 2011), it is simply based on the time gap between actual bus arrival or departure and the scheduled ones. Regularity on the other hand, accounts for the headway adherence of the service. From a passenger point view, and when bus services are running with high frequency, regular and short time headways between buses have a high value to the passengers, as passengers appear to arrive to bus stops in a random pattern rather than checking the published timetables of the buses (Barabino, Francesco, & Mozzoni, 2015), where adequate regularity ensures proper load distribution onboard each bus.

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Many literatures were found to suggest a variety of approaches to evaluate service reliability, using either measures of on-time performance, headway adherence, cancelled trips, or travel time deviations. The Transit Capacity and Quality of Service Manual (TCQSM) by the Transportation Research Board (2013) provides thorough examples of reliability measures with a main focus on on-time performance and headway adherence measures with ranges of Level of Service (LOS) (Transportation Research Board, 2013), where those two measures are the most common ones used by public bus operators (Chen, Yu, Zhang, & Guo, 2009).

Camus, Longo, & Macorini (2005) argued that although the TCQSM method is user-friendly and clear, it may produce inconsistent LOS estimations for three reasons: first, the TCQSM method considers bus delays as a binary variable rather than accounting for the actual amount of the delay, second, it does not adequately evaluate the effect of early bus departures on passengers, finally, the TCQSM method assumes a fixed range of time period for bus departures to be considered on time, that is 3 minutes before the planned departure until 5 minutes after the planned departure rather than having an adjustable tolerance range. The proposed new service measure by Camus, Longo, & Macorini (2005) is called the ‘weighted delay index’ and it addresses these limitations with new LOS ranges, using automated vehicle data.

Several of the studies that addressed service reliability measures were found to address the causes that would result in low service reliability. TCQSM provided factors that affect service reliability, some of those factors are controlled by the bus operator while others are not. Those factors include the route length, number of stops, traffic conditions, road conditions, vehicles’ maintenance, vehicles and staff availability, operator driving skills, frequency of wheelchair’s ramp usage and the size of the operations control strategies that are used to mitigate those factors (Transportation Research Board, 2013). Some of those factors were found to have more severe effects than others, for instance, the route length is the factor with the greatest influence on reliability (Chen, Yu, Zhang, & Guo, 2009; Sterman & Schofer, 1976; Abkowitz & Engelstein, 1983; Strathman, et al., 1999).

The doctoral dissertation of Niels van Oort (2011) presented a comprehensive study on urban public transport reliability, it provided two types of service reliability; one related to bus terminal departure variability and the other is related to trip time variability. The study suggested the causes of each type and recommended instruments to mitigate service irregularities on both operational and planning levels. Figure 2-1 presents different causes for bus terminal departure time variability that are divided into internal causes and external causes. Internal causes can be addressed in the planning and operations processes, which include vehicle and crew availability, schedule quality, driver behaviour and most importantly, infrastructure design configuration (Oort, 2011). The focus here is shed over the infrastructure design configuration and the aftermaths of capacity limitation in bus terminal facilities.

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Figure 2:1: Causes of service variability (Oort, 2011)

The literature review revealed a lack of recognition for the role of strategic planning in improving service reliability, and an existence of substantial amount of studies that focus on operational and tactical interventions. Although in practice, the effects of insufficient bus terminal capacity on service reliability have been successfully remedied with operational instruments, appropriate design choices on the strategic level can create optimum conditions that would maximise the efficiency of operational instruments and prevent unreliability of the service (Oort, 2011). Table 2-1 presents preventive instruments on different levels, where this study will investigate the instruments of bus terminal design which are highlighted in table 2-1. Bus terminal capacity limitations, especially under increased service intensity, are a major factor for service variability, and it can be safe to argue that with increased limitations of bus terminal capacity the more operational and tactical interventions are needed to preserve a required level of service reliability. Later in section 3.4, the extent of operational and tactical instruments used by public bus operators for a specific bus terminal with a specific service intensity, is considered an indicator for the capacity limitations that this particular bus terminal undergoes.

Table 2-1: Preventive instruments of service reliability (Oort, 2011), the table continues on the next page.

Instrument Level Causes affected Training & education of drivers Operational/Tactical Drivers’ behaviour - crew availability

Passenger education Operational/Tactical Passenger behaviour Spare drivers Tactical Crew availability

Vehicle maintenance & spare vehicles

Tactical Vehicle availability

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Instrument Level Causes affected Trip time determination Tactical Schedule quality Interior design vehicle Strategic Vehicle design Exterior design vehicle Strategic Vehicle design Priority at traffic lights Strategic Other traffic

Platform design Strategic Passenger behaviour Terminal (capacity) design Strategic Infrastructure configuration

Stop (capacity) design Strategic Infrastructure configuration Exclusive lanes Strategic Infrastructure configuration

Coordination of lines Strategic Service network configuration Length of lines Strategic Service network configuration

Stopping distance Strategic Service network configuration Synchronisation of lines Strategic Service network configuration

2.3. Design of Bus Terminals

Efficient public bus services require a well-built system that offers a greater primacy to these services over other traffic that share its facilities. Competition for space has been a challenge for bus traffic planning and operations, where adequate transport policies permit increased accessibility for bus traffic over other vehicular road users, such attempts come in the form of designated bus lanes and by transit signal priority (TSP) that grants priority for buses at signalised junctions. A poorly planned or designed facility can be a threat to the attractiveness of the service. The localisation and design of bus terminals come with a high importance to the performance of bus transport systems, it should not only create optimum conditions for bus operations, but it should also consider passengers and nearby habitants surrounding the facility (Stockholms läns landsting, 2019).

The Public Transport Administration in Stockholm county (Trafikförvaltningen) provided guidelines for establishing well-functioning and efficient bus terminals (Stockholms läns landsting, 2019). The four guidelines suggested by The Public Transport Administration are the following:

x Physical bus terminal location: A bus terminal facility should be recognised as a long-term structure and it should be functional for the foreseeable future, which makes its location a crucial matter. The localisation of a terminal considers both traffic operation and passengers’ comfort. If intermodality is to be permitted in a terminal, bus services should be well-connected to other transport services. If bus services are not to be connected with rail services, there should be direct connections to important destinations such as the city centre.

x Bus terminal capacity: The capacity of a bus terminal should accommodate for future development and an increase in demand for bus services. Land acquisition for a bus terminal establishment should take into account the availability of space that would not limit the potential increase in bus service intensity and its required capacity.

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x Seamless structure: A bus terminal facility should allow for seamless and smooth movement of passengers and vehicles. This requires elimination of physical boundaries within a bus terminal that would be dividing the bus terminal. Physical boundaries could reduce flexibility of bus movement or increase distance for travellers. The walking distance within the terminal between different modes of transport should not exceed 200 metres and the walking distance between bus stops should not exceed 150 metres. Trafikverket (2013) recommends that the terminal design should allow customers to transfer between transportation modes in a safe manner, without having to enter areas with frequent vehicle movements

x Level difference: Height difference between platforms of different public transport modes within a terminal should be avoided as much as possible, as it would result in reduced accessibility for travellers and increased transfer times. Escalators and lifts should be provided to enhance accessibility between platforms with different altitudes.

2.3.1. Classifications of Bus Terminals There are several types of bus terminals, the best general design in each project depends on the intended functionality of the bus terminal, available space dedicated for bus manoeuvres and the bus traffic volume to be served in the bus terminal. (Gunnarson & Lindqvist, 1988; Sveriges Kommuner och Landsting, 2015) described types of bus terminals according to their functionality and localisation as the following:

x Regional bus terminal: It is a bus terminal that serves lines that connect the central city with surrounding regions, it should be located in a central location where rail services are provided, and it should allow for transfers to local buses and trains. Since this type of bus terminal is expected to have a large number of travellers intending to change their mode of travel, safety of pedestrians and cyclists should be considered when planning the location and the design of bus stops.

x Local bus terminal: This type of bus terminal intends to serve local bus lines, where their stops are located directly outside the station. The stops location can be shared with regional services if time regulation is not intended to happen there. Local bus terminals should be located in an urban centre and it can serve both terminating and non-terminating lines.

x Feeder bus terminals: A type of bus terminal that provides connections between feeder bus services and other long-distance public transport services that is located in a suburban centre. Table 2-2 provides more details on the characteristics of regional, local and feeder bus terminals:

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Table 2-2: Characteristics of regional, local and feeder bus terminals (Translated from Gunnarson and Lindqvist (1988))

Regional bus terminal Local bus terminal Feeder bus terminal

Localisation City centre with rail services City centre Suburb centre

Accessibility Close to businesses and workplaces within downtown

Close to businesses and workplaces within downtown

Close to businesses and workplaces within suburban centre

Functionality Connections to regional and long-distance services

Connections to local bus lines

Connections between feeder bus lines and other public transport

Traffic served Terminating lines with variations in frequencies.

Non-terminating lines with high frequencies

A majority of terminating lines, most of travellers change their bus line.

Pedestrians Many directions Many directions Movements to connections

Passenger service requirement Large Large Limited

Freight handling Occurs, in some cases in large size

No No

Taxi rank within terminal premises Yes Yes Sometimes

Establishing a bus terminal requires involvement from a variety of stakeholders, and its design is bounded by several resources. One important limitation is the available space that can be dedicated for the bus terminal facility, a balance should exist between level of service provided by the bus terminal and the land acquisition assigned for bus terminal services, especially when the facility is to be constructed in an expensive real estate market. The many design options for bus terminals make it possible to design bus terminals better tailored to the needs of each bus terminal, its service characteristics and its limited space. Gunnarson & Lindqvist (1988), Transportation Research Board (2000), Sveriges Kommuner och Landsting (2015) and Askerud & Wall (2017) provided a description of the different design alternatives for bus terminals. Gunnarson & Lindqvist (1988) provided two basic principles for the needed bus manoeuvres to enter and exit a bus stop:

x Exclusively forwarding movement: Entering and exiting a loading area does not require the bus to perform a reversing movement, which has a great reflection on pedestrians’ and cyclists’ safety as blind spots for bus drivers are minimised.

x Docking movement: The bus is required to move backwards when entering or exiting the loading area, similar to a ship that comes into a dock and is tied up at a wharf. Although docking movement is less safe than only forward movement, it provides a great opportunity to optimise the surface area, but on-site traffic management is needed to ensure safe traffic movements.

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A bus loading area (berth) is defined as the space dedicated for one bus to stop and allow for the passengers to board or alight, and a bus stop can be composed from one or several adjacent loading areas (Transportation Research Board, 2000), an illustration is presented in Figure 2-2.

Figure 2:2: Illustration of bus loading areas, stops, and facilities (Source Transportation Research Board,

2000)

In accordance with the two basic principles provided by Gunnarson & Lindqvist (1988), categorisation of the bus stop characteristics can be presented as described in the two following sub-sections of this report.

Bus Terminal Designs with Exclusively Forwarding Movement Loading Areas In this section bus terminal layouts with exclusively forward-going movements at the bus stops are described.

a) Linear loading area

A bus stop with several linear loading areas permits buses to align it selves behind each other, if the distance between the loading areas of this type is short, the front bus might block the bus behind and it might not be possible for the bus behind to depart independently, meaning that the bus in front must exit the stop to allow for the following bus to exit. This type of bus formation is suitable when buses do not need to wait for too long in the bus loading area. Linear loading areas allow buses to cover their whole length along the kerbside which increases comfort for riders when boarding and alighting (Gunnarson & Lindqvist, 1988; Transportation Research Board, 2000). Figure 2-3 presents a bus terminal with linear loading areas.

Figure 2:3: Buss loading area with a linear design (Source Transportation Research Board, 2000)

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b) Sawtooth loading area

This type of bus stop has its loading areas aligned in a sawtooth pattern as presented in Figure 2-4, where the buses can be positioned at a slightly oblique angle from the main direction of the street, which could allow buses to enter and exit independently from each other and without the need to do any reverse movements. Another advantage of this design is that buses can display both front and side information towards passengers’ walkways. The drawbacks of this setting is that it requires more linear length than a linear loading area of around thirty-three percent to serve the same number of buses (Transportation Research Board, 2000) and it can be considered as a unattractive design (Sveriges Kommuner och Landsting, 2015).

Figure 2:4: Buss loading area with a sawtooth design (Source Transportation Research Board, 2000)

c) Drive-through loading area

Bus stops with Drive-through loading areas is suitable for bus terminals with limited space, where it is composed of several parallel and adjacent berths that come in the form of islands between buses, as presented in figure 2-5. This setting allows a bus to align its door-side against the island and then exit by driving through. The islands can be designed in a parallel direction or angled with the main direction of the street. One disadvantage of this arrangement is that passengers have to cross bus driving surfaces, which makes it important for safety reasons to consider minimising the distances of pedestrian crossings and ensure clear signage for pedestrians and buses (Gunnarson & Lindqvist, 1988; Transportation Research Board, 2000; Sveriges Kommuner och Landsting, 2015).

Figure 2:5: Buss loading area with a drive-through design (Transportation Research Board, 2000)

Bus Terminals with Docking Movement Loading Areas A docking movement loading area is usually referred to it as an angle loading area. One can consider a bus stop of Angle loading areas as an extreme case of the sawtooth design but without necessarily covering the whole bus door-side against with a kerb

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edge, where the angle of the loading area is large enough that buses need to reverse to exit. Although it is disadvantageous for the bus to reverse as it is unsafe for pedestrians and cyclists due to low visibility for the driver, this type of setting can accommodate a greater number of buses than a sawtooth or linear design. Docking is suitable when buses need to occupy the loading area for a relatively long period. Figure 2-6 presents a bus terminal with loading areas of an angled design. Stops with angled loading areas can be set at any desirable angle, in some cases perpendicular from the main street direction, where the larger the angle gets the more buses can be accommodated for the same linear length, but that will create a need for further space behind the buses that is required to reverse when they leave their berths. If insufficient space is provided, departing buses disrupt the traffic flow when they are leaving their berths, which can cause delays and decrease bus terminal capacity. For this type of loading areas, pedestrian movements should be controlled for safety purposes (Gunnarson & Lindqvist, 1988; Transportation Research Board, 2000; Askerud & Wall, 2017).

Figure 2:6: Buss loading area with an Angle design (Source Transportation Research Board, 2000)

Bus terminal types prominently used in Sweden Practically in Sweden, public bus operators and public transport authorities have a simpler classification of bus terminals that depends on the general bus terminal layout in terms of its islands’ configuration. Gunnarson & Lindqvist (1988) have described three configurations which are currently used in Sweden: Central platform bus terminal, Street bus terminal and the previously mentioned drive-through and docking bus terminals.

x Central platform bus terminal: A Central platform bus terminal is configured by a large sized platform that represents several loading areas for bus lines of different directions, and bus traffic is circulating around the central platform. Although this type of configuration requires large space, it has a strong advantage presented by its safety, where travellers do not need to cross any street to reach the bus service (Gunnarson & Lindqvist, 1988; Askerud & Wall, 2017). It is common for this bus terminal configuration to have a protected connection to rail services where escalators or stairs in the middle of the bus platform can reach to a subway that stretches to underground or over-ground train platforms. Having bus and train platforms at different levels increases the comfort of travellers and minimises their mode transfer time, as they will need to use escalators or the stairs only once rather than twice if both platforms were on the same level. Figure 2-7 shows an example of a Central platform bus terminal with a tunnel connecting to a train platform.

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Figure 2:7: Central platform bus terminal with connection to train services

x Street bus terminal: It is the simplest type of bus terminals where buses stop directly on the street, it is usually used in a combination with a real bus terminal area to accommodate lines with a majority of non-terminating services. This configuration uses existing street space and it does not require large land acquisition. Acquiring service information by travellers is limited with this configuration and it can cause confusion for bus users, especially if this type of bus terminal is in a combination with a bus terminal of bus dedicated area. Safety accompanied with street bus terminals is considered to be low as pedestrians need to cross the street to board buses on the other direction of the street (Gunnarson & Lindqvist, 1988). Figure 2-8 shows an example of a simple street bus terminal.

Figure 2:8: Example of a street bus terminal

2.4. Capacity of Bus Terminals

Capacity issues of public bus services are of vital importance. As populations grow, together with today’s transport policies which are centralised around increasing the penetration levels of public transport market share (Sveriges riksdag, 2014), the demand for bus services and its facilities is expanding. Where this demand will eventually, if not already, be bounded by the capacity of its facilities, causing a threat to the attractiveness of the service. Capacity matters are essential for public transport agencies and transport planners as it has an influence on service quality indicators such as service speed and its reliability as mentioned in section 2.2. Evaluations of capacity limitations of bus services are also essential for forecasting the effects of changes in public transport systems and comparing short-term savings with long-term costs for strategic planning (Transportation Research Board, 2013).

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The Transit Capacity and Quality of Service Manual (TCQSM) provided two definitions of public transport capacity, persons capacity and vehicles capacity, where the latter is more related to the research area of this study. Vehicle capacity of a bus terminal according to Transportation Research Board (2013) can be defined as the maximum number of buses that can be served by a bus terminal during a period of time with a given level of reliability. In this section, literatures that focus on the capacity of bus stops and capacity of bus layover facilities will be reviewed.

2.4.1. Capacity of Bus Stops in Bus Terminals An important aspect in evaluating a bus terminal’s capacity is to determine the capacity of the facilities that are involved in the processes of boarding and alighting of passengers. The Highway Capacity Manual (HCM) has introduced a deterministic model for calculating bus capacity components of three key locations related to boarding and alighting of passengers of which can be adopted for bus terminals, these locations are bus loading area, bus stops and bus facility. The definitions of these locations are found in section 2.3.1 and is visualised in figure 2-2. The Highway Capacity Manual model is:

𝐵 = 𝑁𝑒 𝐵 𝑓𝑡 = 𝑁𝑒 𝑓𝑡3600(𝑔 𝐶⁄ )

𝑡𝑐 + 𝑡𝑑(𝑔 𝐶⁄ ) + 𝑍𝑐𝑣𝑡𝑑 (1)

Where

𝐵 = bus stop capacity (bus/h),

𝑁𝑒 = number of effective loading areas at the bus stop = 1.85 for double loading areas,

𝐵 = individual loading area bus capacity (bus/h),

𝑓𝑡 = traffic blockage adjustment factor,

𝑔 𝐶⁄ = green time ratio (the ratio of effective green time to total traffic signal cycle length, equals 1.0 for unsignalised streets and bus facilities),

𝑡𝑐 = clearance time (s),

𝑡𝑑 = average (mean) dwell time (s),

𝑍 = standard normal variable corresponding to a desired failure rate, and,

𝑐𝑣 = coefficient of variation of dwell times.

The model provided by the Transportation Research Board (2000) for bus stop capacity calculation suggests that the capacity of a bus stop depends on the number and the capacities of the loading areas that it holds. Naturally, the more loading areas a bus stop has the higher the capacity gets, but the capacity of a bus stop does not equal to the summation of individual loading areas’ capacities.

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Addressing this effect, the HCM introduced the number of effective loading areas concept (𝑁𝑒 ), which decreases the total capacity of the stop from the summation value of individual loading areas’ capacities. The severity of this factor decreases if buses can use the bus stop without blocking the travel lane or if buses arrive in platoons. The Highway Capacity Manual recommends using another adjustment factor for calculating the bus stop capacity, that is the traffic blockage adjustment factor (𝑓𝑡 ). This factor accounts for capacity reduction due to green time lost by buses that need to wait for vehicles and pedestrians that are conflicting with the bus lane at a signalised intersection, if such conflicts do not exist this factor can be omitted.

The model suggests three factors that determine the capacity of a single loading area (𝐵 ); dwell time, clearance time and failure rate, with considerations for nearby traffic signals that would lower the bus stop capacity with low effective green to cycle length ratio (𝑔 𝐶⁄ ) of which limits buses’ opportunities to enter or exit the stop (Transportation Research Board, 2013).

x Dwell time: Is the total time a bus spends at a loading area to serve the passengers boarding and alighting, and this includes the time spent opening and closing doors. Where the coefficient of variation of dwell times (𝑐𝑣) accounts for the variations in dwell times that numerically equals to the standard deviation of dwell time divided by mean dwell time, with a recommended value of 0.6.

x Clearance time: Is the time needed for a bus to drive away from the bus stop and clear enough space for the following bus to enter the stop, this time also includes the waiting time for a gap to re-enter the travel lane if needed.

x Failure rate: The failure rate incorporates the probability a bus will find the loading area occupied by another bus upon its arrival, where the higher the failure rate the higher the capacity of the loading area is, but with a decrease in service reliability.

Al-Mudhaffar, Nissan and Bang (2016) argued that the model provided by the Highway Capacity Manual (HCM) has three limitations that need to be addressed. First, the model does not account for the time needed by a bus to manoeuvre and decelerate to enter the bus stop, it only recognises time variables that take place afterwards. Second, the model considers the effect of a downstream signal on the capacity of the stop, but it does not recognise the effect of pedestrian traffic and speed reduction measures downstream as a cause for capacity reduction of the stop. Finally, the model neglects the effect of distributed arrival patterns such as bus platoons.

Considering those limitations, and as Swedish literature showed little knowledge in this area of study, the article of “Bus stop and bus terminal capacity” by Al-Mudhaffar, Nissan and Bang (2016) presented an analytical model adjusted for bus operations in large Swedish cities that was based on the HCM model, which was later practically implemented through Stockholm County’s bus terminal design guidelines (Stockholms läns landsting, 2019). Their field study was conducted on five bus terminals in Stockholm county that included single and double loading area per stop of a linear

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design and angle loading areas, where none of the locations were affected by a downstream traffic signal. By comparison and evaluation of the Highway Capacity Manual model with field data, Al-Mudhaffar, Nissan, & Bang (2016) provided this analytical model for a bus stop that has two loading areas:

𝐵𝑠𝑎𝑝 = 𝐹𝑎𝑝𝐵 𝑎𝑝 = 1.7 3600(0.4 + 𝑡𝑑

1000)𝑡𝑐 + 𝑡𝑑 + 𝑍𝑐𝑣𝑡𝑑

(2)

Where

𝐵𝑠𝑎𝑝 = applied bus stop capacity (bus/h),

𝐹𝑎𝑝 = applied effective loading area for a bus stop of double loading areas = 1.7,

𝐵 𝑎𝑝 = applied maximum number of buses per berth per hour (buses/h),

𝑡𝑐 = clearance time (s),

𝑡𝑑 = average (mean) dwell time (s),

𝑍 = standard normal variable corresponding to a desired failure rate, and,

𝑐𝑣 = coefficient of variation of dwell times.

By following the HCM guide lines for determining the “maximum capacity” of a bus stop, Al-Mudhaffar, Nissan, & Bang (2016) set a failure rate of twenty-five percent as a condition for the measured capacity in the field. By comparing the observed number of buses served per hour with the number provided by the HCM, a correction factor was recommended at a value of 0.4. Where this correction factor is dependent on dwell time and can be described by this relationship:

𝐶𝐹 = 0.4 +𝑡𝑑

1000 (3)

By comparing equation (2) provided by the HCM and equation (3), the effective loading area for a bus stop of double loading areas has been reduced from 1.85 to 1.7. Also, the effective green to cycle length ratio (𝑔 𝐶⁄ ) has been omitted from equation (3). Al-Mudhaffar, Nissan, & Bang (2016) argued that their model considers the effect of adjacent pedestrian crossings and speed reduction measures on the clearance time, a limitation that existed in the Highway Capacity Manual model. Moreover, the correction factor (𝐶𝐹) in equation (3) considers the conditions of public buses operations in large cities such as the effect of bus platoon arrivals.

To estimate the total bus terminal capacity, Al-Mudhaffar, Nissan, & Bang (2016) recommends two methods. An empirical analysis or simulation of bus operations in the bus terminal. The provided empirical approach suggests that the total bus terminal capacity equals to the summation of the capacities of the bus stops within the bus terminal, through implementation of equation (3). Where this approach assumes that buses operates independently from each other, this can be a valid assumption only in

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bus terminals with a large manoeuvring area relative to the service volumes. If larger traffic loads are expected in the bus terminal, a conflict between buses might arise inducing reduction in the total bus terminal capacity that needs to be accounted for by deducting this capacity drop from the summation value of the capacities of each bus stops within the bus terminal (Al-Mudhaffar, Nissan, & Bang, 2016).

Conflicts that result in capacity drop in a bus terminal are due to formulation of bus queues, blockage of entrance or exit by buses served from other stops, passengers’ movements on surfaces dedicated for driving, narrow bus terminal exit or entrance, large bus terminal manoeuvre area that results in extended travel time from buses to enter or exit the facility (Adhvaryu, 2006; Al-Mudhaffar, Nissan, & Bang, 2016).

2.4.2. Capacity of Bus Layover Parking As the literatures in sections 2.1 and 2.2 have presented, reliability and punctuality of bus services are crucial for the attraction of ridership and the competitiveness against private vehicles. A common practice by public bus operators that promotes the reliability of a service is to avoid tight vehicles’ schedules. An appropriate scheduling of the vehicles should consider the possibility that a bus might run shortly behind its schedule by adding extra time that would allow the bus to catch up and to be on time for its next departure. This extra time is referred to as slack time, where the higher the slack time is, the higher service reliability becomes as it becomes easier to eliminate delays propagation (Oort, 2011).

For a certain vehicle fleet size, an excessive increase in slack time in vehicles schedules can reduce service frequency and encourages drivers to delay their departures, thus a carefully selected slack time value should be added to the schedule, where the slack time can be added to the dwell time, driving time or layover time (Zhao, Dessouky, & Bukkapatnam, 2006; Oort, 2011). Considering the fact that a slack time is a non-productive time for both the vehicle and the driver, the amount of slack time added to a vehicle schedule is directly associated with the operational costs and the resulted delay penalty fine, where a large slack time added would result in an increase in the operational costs of running a service, and a small slack time value can expose public bus operators to penalty fines due to delays in the service provided.

Regulations allow bus drivers to take breaks after a certain amount of time of continuous driving for driver’s comfort and travellers safety. The bus drivers must have a pause of at least ten minutes and a break of at least thirty minutes within specified timeframes (Stockholms läns landsting, 2019). During these breaks, the bus driver is allowed to leave the bus and to park it in a certain location in the bus terminal, this location is referred to as a bus layover parking. Layover time can be defined as the time included in a bus schedule where passengers are not on board. A layover is necessary when a recovery time or a driver’s break is scheduled (Transport for New South Wales, 2018). In cases where the line terminates at a bus terminal, the layover time can include both the driver’s break and the slack time intended for schedule recovery.

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Capacity limitation in layover parking facilities in bus terminals is a threat to the service reliability as it effects the achievability of the service schedule. Bus lines that terminate in a bus terminal suffering from an insufficient layover parking capacity require a larger number of vehicles to run its services in a certain frequency, as buses will need to drive further to another location that offers a layover parking and the time required to complete a round trip will finally be increased (Transportation Research Board, 2013). Acknowledging the association of layover parking capacity with the quality of service delivered and the costs associated with its limitations, the need to have an appropriate method to estimate the size of layover parking in bus terminals becomes essential.

The Transit Capacity and Quality of Service Manual provided guidelines for estimating the maximum required bus loading areas (berths) in a bus terminal, including those that are dedicated for layover activities (Transportation Research Board, 2013). The method suggests that the maximum number of bus berths for a route depends on the recovery time and the time headway between successive buses. The manual also recommends that variations of recovery times and time headways should be considered, and the combination that would result in the largest number of berths should be selected. The relationship that describes the maximum number of berths dedicated for layover can be expressed as the following:

𝑁 𝑒𝑟𝑡ℎ𝑠 = 1.2 𝑡 𝑎𝑦𝑜𝑣𝑒𝑟

𝑡ℎ𝑒𝑎𝑑𝑤𝑎𝑦 (4)

Where

𝑁 𝑒𝑟𝑡ℎ𝑠 = number of berths (loading areas) required for layover,

𝑡 𝑎𝑦𝑜𝑣𝑒𝑟 = layover time at the bus terminal, including slack or recovery time,

𝑡ℎ𝑒𝑎𝑑𝑤𝑎𝑦 = time headway between arrival of terminating services.

The method proposed recommends providing additional berths for alighting of passengers before the bus is displaced to the layover parking. For instance, if equation (4) produced a result of 1 berth, that berth will be sufficient for alighting of passengers and layover activities before the succeeding bus arrives, and additional alighting loading areas should be considered if the service volume required so. Loading areas dedicated for boarding of passengers should be estimated separately, where the Transit Capacity and Quality of Service Manual suggests one loading area for boarding of passengers per route that have terminated.

The factor of 1.2 is included in the formula to account for early arrival buses. The manual also recommends that future growth should be taken into consideration for the number of routes terminating and the substituted values of 𝑡 𝑎𝑦𝑜𝑣𝑒𝑟 and 𝑡ℎ𝑒𝑎𝑑𝑤𝑎𝑦. The method proposed by Transit Capacity and Quality of Service Manual together with equation (4) is logical, where the increase in both frequency (1 𝑡ℎ𝑒𝑎𝑑𝑤𝑎𝑦⁄ ) and layover time generates more need for layover parking. Yet, the method assumes that buses

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terminating their service at a bus terminal will return over their route in the other direction after its layover time. Although this indeed takes place in numerous cities due to its simplicity in vehicle scheduling, yet this scheduling method is not frequently used in many countries such as Australia, United Kingdom and Sweden, where buses are scheduled to serve a new line after its layover time and not to return in the opposite route direction, i.e. buses change their line number and the trip information presented to the new travellers. This commonly employed scheduling technique by public bus operators is called interlining (Blais, Lamont, & Rousseau, 1990).

Interlining technique in vehicle scheduling allows public bus operators to optimise their resources such as reducing their fleet size and their operational costs in addition to reducing passenger waiting times as the service becomes more demand responsive (Blais, Lamont, & Rousseau, 1990; Oort, 2011; Gkiotsalitis, Wu, & Cats, 2019). Interlining can be a complex technique that is difficult to schedule by bus operators, and it can result in driver’s discomfort as it requires drivers to be alert to their bus schedule since their vehicle block is non-repetitive and hard to predict. This may explain the reason that some cities do not allow interlining or limit its usage (Blais, Lamont, & Rousseau, 1990). When a bus is operating on an interlined route, it is common that the bus will need to deadhead to a new bus terminal to start serving a new line there, thus one can consider layover time, deadheading and interlining to be unproductive time; that is when passengers are not being served on board the bus (Oort, 2011).

A vehicle block is the product of vehicle scheduling, which shows all the assignments required by a certain bus from the time it leaves the bus depot (garage) until the time it returns back to it after its daily operations are concluded (Blais, Lamont, & Rousseau, 1990). The complexity of bus scheduling where interlining is allowed demands a need for using computer-based optimisation tools, in such cases a vehicle block is commonly a product that is produced by a software which optimises daily vehicle and crew assignments simultaneously (Haase, Desaulniers, & Desrosiers, 2001), such as Giro Hastus. Whereas deadheading is the travel made by a bus with no passengers on-board (Blais, Lamont, & Rousseau, 1990). Practically in Sweden, to promote reliability of the service, buses that terminate their service in one bus terminal to start serving a new line in another bus terminal, is scheduled to have their layover time in the second bus terminal where they are close to their departure loading area.

Considering the utilisation of interlining in many countries, the model suggested by TCQSM will have an inaccurate estimation of the requirements of a layover parking in a certain bus terminal as buses will layover in another bus terminal where their next departure is scheduled. The complexity of bus operations where interlining is allowed and buses’ schedules are a product of optimisation tools, makes it quite difficult to use analytical approaches to estimate suitable capacity of layover parking facilities. Further discussion on limitations of analytical models is presented in section 3.1.3. Another limitation in the TCQSM model is that it does not consider the effect of intermodal connections between buses and other rail-based modes of transport. Where for bus

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terminals that offer access to rail-based transport services, public bus operators are bound by a contract with transport authorities to provide enough time window for bus users to board certain train (in some cases trunk bus lines) departures or to collect passengers arriving from certain train arrivals, these time-connections become increasingly apparent with lower frequencies. When such requirement exists for a certain train service, there will be an increase in the number of buses waiting for the arrival of this train, and this will increase the need for layover parking in that bus terminal.

Similar to The Public Transport Administration in Stockholm County, Transport for New South Wales (TFNSW) in Australia has recognised the need to identify the requirements of layover parking in bus terminals, and that processes of future transport investments, especially for bus facilities that offer intermodal connections, acknowledge the necessity to negotiate the arrangement and land acquisition needed for bus layover activities in order to deliver an attractive service (Transport for New South Wales, 2018). TFNSW have provided guidelines for design and configuration of layover parking, where they have presented an analytical model that describes the required capacity of layover parking in bus terminals as follows:

𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡 = 𝑁𝑎 − 𝑁𝑠 (5)

Where

𝑁𝑎 = number of buses scheduled to arrive,

𝑁𝑠 = number of available bus stops.

According to Transport for New South Wales (2018), the capacity requirement indicates the capacity estimated for the peak hour, where equation (5) suggests that the capacity needed for layover parking is the number of arriving buses that exceeds available bus stops. The guidelines provide that equation (5) is general and results in an unconstrained solution, where for certain bus terminals constraints should be considered later on when using the model. One can argue that equation (5) has few limitations that would result in a non-optimal solution, unlike the TCQSM model in equation (4), equation (5) does not properly consider the arrival pattern of buses. Short time headways between arriving buses can increase the demand for layover parking, and time headway of arriving buses can variate much during one-hour. Also, the guidelines do not indicate which proportion of those arriving buses will terminate their trip in that bus terminal.

As previously discussed, when interlining is allowed in a country, such as Australia, vehicle scheduling becomes quite complex that a scheduling software such as Giro Hastus becomes necessary, and the appointed location for a bus to have its layover time in depends on the choice the software suggests which should result in better optimisation of the resources and its associated operational costs, this can generate uncertainty in the desired location for a bus to have its layover in before it schedule has been planned, which suggests that it is not necessarily that a bus is scheduled to have

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its layover time in the same bus terminal that it terminates its services in, as that would be a threat to the service reliability, and can cause delays in the first departure of the new line and delay propagation in the following ones in that line.

Moreover, similar to the limitation of the Transit Capacity and Quality of Service Manual model, equation (5) does not consider the increase of layover parking requirements in bus terminals that offer time connections between buses and other rail-based modes of travel. Where buses from different lines need to have their layover times all together at certain times of the day, these extra layover times ensure enough time window for travellers to reach the platform of other modes of travel by normal walking speed and catch their connection. Whether it is to reach the train or the bus service, extra layover capacity is needed to be accounted for when modelling the need for layover parking facilities.

2.5. Reflection on the literature

Reviewing previous literature revealed a lack in the knowledge of proper capacity required for bus layover parking, although this issue has an impact not only on the operational costs that bus operators must endure on daily basis, but also on the quality of service delivered to travellers, and this can be a challenge to promoting sustainable mobility and lowering the car dependency that has been set as a futuristic goal for many cities. The literature showed the importance of bus services in intermodal travel, and that it is the backbone of public transport services, as it offers flexibility in routing that is responsive to demand variations in both time and space. Bus services are essential in intermodal travel, and such quality demands reliability of its service.

The literature exhibited the correlation that exists between quality of bus services and the design and configurations of bus facilities, especially capacity requirements in bus terminals, since bus terminals represent the starting point of the service where punctuality is mostly needed for establishing timely fashioned connections and eliminate sources of service delay propagations along bus routes. Understanding capacity limitations in bus terminals is a vital matter to successfully provide conditions that allow for service development, where capacity issues should not only account for today’s need but should also consider the fact that demand for bus services is increasing along with population growth.

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3. CASE STUDY OF STOCKHOLM

Stockholm, the capital of Sweden is a vibrant and dynamic city, it is the largest of the five capitals of the Nordic countries. Stockholm is forecasted to have a 25 percent growth of its inhabitants by 2030 (The City of Stockholm Traffic Administration, 2012). Stockholm county is the metropolitan area that surrounds Stockholm city, it has one of the highest Gross Regional Product (GRP) per inhabitant in Europe, in fact, its GRP is seventy-four percent higher than the average in the European Union (Stockholm Business Region, 2017). Stockholm county is the home for more than one-fifth of the total population of Sweden, setting a real challenge for the transport system. Recognising the large growth and to release the city’s potentials, Stockholm’s city urban mobility plan has focused on three cornerstones (The City of Stockholm Traffic Administration, 2012); urban planning, infrastructure planning and traffic planning, where the latter two put an emphasis on investments in public transport systems generally, and the public bus network specifically. Region Stockholm is the entity that is responsible for organising public transport in Stockholm county, most of which is run under the SL trademark. SL has adopted a single zone ticketing system that covers all public transport systems in the county, promoting the multimodal travel experience. This chapter exhibits some distinctive properties of public bus operations around and within the bus terminals in Stockholm county that could influence the need for layover parking capacity. This chapter also serves as an introduction for the methodology chapter.

3.1. Public Bus Network

In this section the structure of the public bus network in the greater Stockholm region is described together with the concept of interlining as a mean of optimising bus operations.

3.1.1. Contract Areas A contract area is a geographical boundary of which its bus lines are procured at the same time with the same legal agreement or contract with a certain public bus operator, contract areas are part of the procurements of bus services. Currently there are eleven different contract areas in Stockholm county. Bus lines in each contract area are procured to a single public bus operator, where currently (April 2020) there are four different operators in Stockholm county: Keolis, Nobina, Arriva and Transdev. A contract area includes a set of bus lines, bus terminals and bus depots that can be seen as a smaller bus network that is part of the larger Stockholm’s county bus network. The map in figure 3-1 shows the boundaries of each contract area as part of the overall Stockholm’s county area. The adjacency of one contract area with another area indicates that the bus lines that form the links of the network can penetrate contract area boundaries when a line starts from one contract area and terminates in a bus terminal within a different contract area. In cases when those adjacent contract areas are procured to different public bus operators, legal agreements can be established between them to regulate the utilisation of the infrastructure dedicated for each

26

operator. For example, each operator can have a certain quota of the layover parking zone in the bus terminal, either by an area quota or by part of the day quota. Table 3-1 provides details about area size, current operator, number of bus terminals, number of depots and municipalities covered by each contract area.

Figure 3:1: Contract areas in Stockholm county (source google Earth)

Table 3-1: Current contract areas in Stockholm county

Contract Municipalities Approx. area (km2)

Bus operator

Bus terminals

Bus depots

E19 B Norrtälje 2,015 Nobina 4 2 E19 HBS Huddinge, Botkyrka & Söderort 483 Keolis 21 3 E19 NV Nacka and Värmdö 462 Keolis 4 4

E20 (1) Sollentuna, Sundbyberg, Solna & Norrort 174 Arriva 11 2

E20 (2) Österåker, Vaxholm, Danderyd, Täby & Vallentuna 882 Arriva 4 4

E22 Stockholm (Innerstad) & Lidingö 103 Keolis 4 3 E23 Tyresö, Haninge & Nynäshamn 911 Nobina 10 4 E27 Södertälje, Salem & Nykvarn 836 Nobina 7 1 E28 Järfälla & Upplands Bro 366 Nobina 5 1 E31 Sigtuna & Upplands Väsby 463 Transdev 2 1 E32 Ekerö 388 Arriva 1 1

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3.1.2. Bus Lines and Bus Terminals The two concepts “bus line” and “bus terminal” are elaborated in this section.

Bus Lines The large bus network of Stockholm county is formed by links of more than 480 bus lines, with eighteen of them being trunk lines (“blue buses”), and by nodes of more than 6580 bus stops, and serviced by a fleet of more than 2200 buses (AB Storstockholms Lokaltrafik, 2019). Bus lines are counted as they are presented in the timetable. The definition of what constitutes a distinct bus line varies depending on area due to factors that are outside the scope of this thesis. Two extremes that have been identified in this study are:

x All trips running along a common corridor or service the same island form a common bus line, e.g. SL line 626 which is used for the whole network on the island of Ljusterö including all trips to and from the island and also trips running along a segment of the corridor between Danderyd and Åkersberga

x Trips of different length along a common corridor are assigned distinct line designations, e.g. line 647 which is a segment of 677, line 301 which is a segment of line 177, 682 which is a segment of 680, and 433 which is a segment of 434

Bus Terminals In this thesis, a bus terminal is defined as a facility where bus lines end or start, or a combination of both. They may be part of an intermodal hub, but they should preferably provide bus drivers with access to suitable facilities for pause or break. Therefore, a bus terminal should provide layover parking and it should be located within immediate vicinity of the personnel service facility (such as a toilet or a personnel room), i.e. within walking distance. In some cases, several bus stops were combined to one in preparation for further analysis. These are presented in table 3-2.

Table 3-2: Bus terminals and agglomerations of bus stops considered as one bus terminal due to their operational or geographical proximity. The table continues on the next page.

Stops combined Description

Danderyds sjukhus Mörby station

Mörby station is a terminus for three bus lines approximately 300 metres away from Danderyds sjukhus. Operationally it is common with deadheadings from Mörby station to Danderyd for driver pause or driver break.

Farsta strand station Farsta strandplan Farsta Strand T-bana

These three stops form the multimodal hub of Farsta strand.

Flemingsberg stn viadukten Flemingsberg stn Huddingevägen Flemingsberg station

These three stops form the multimodal hub of Flemingsberg, previously known as Stockholm Syd.

Hallunda T-bana Hallunda centrum

Two stops forming the hub of Hallunda.

Handen station Handenterminalen Haninge centrum

Normally only Handen station and Handenterminalen form the hub of Handen but due to operational changes connected to the development of a renewed hub also Haninge centrum is included as part of the temporary setup for the hub.

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Stops combined Description Odenplan Odenplan station

These two stops form the multimodal hub of Stockholm Odenplan.

Sundbyberg station Sundbyberg centrum These two stops form the multimodal hub of Sundbyberg.

Upplands Väsby station Upplands Väsby stn västra

These two stops form the hub of Upplands Väsby.

Vallentuna station Vallentuna station västra

These two stops form the hub of Vallentuna.

Table 3-3 contains an excerpt of the data collected on bus terminal characteristics in the greater Stockholm region. The complete data set is presented in appendix A.

Table 3-3: Sample of bus terminals characteristics.

Alvi

k T-

bana

Bran

dber

gen

cent

rum

Brom

map

lan

T-ba

na

Fars

ta T

-ban

a

Hud

ding

e st

atio

n

Hög

dale

n T-

bana

Jako

bsbe

rg s

tatio

n

Jord

bro

stat

ion

Kallh

äll s

tatio

n

Karo

linsk

a sj

ukhu

set

norr

a Ki

sta

T-ba

na

Kung

ens K

urva

Mär

sta

stat

ion

Nor

rtäl

je b

usst

atio

n

Nyn

äsha

mn

stat

ion

Terminal stops 3 3 8 5 9 3 10 2 3 7 5 6 6 11 4 Street stops 0 2 0 2 0 2 3 2 1 0 1 0 0 0 0 Dedicated layover berths 0 0 0 0 3 2 7 0 0 6 0 0 0 7 0

Out-of-use bus stops 1 1 1 0 0 0 0 0 1 4 0 0 0 0 0 Kerbside parking (m) 40 0 210 40 100 0 80 60 100 0 100 40 240 0 100 Starting lines 3 7 17 6 7 4 11 4 5 8 12 4 19 24 4 Strictly continuing lines 0 3 2 2 1 3 0 0 0 3 5 4 2 0 2

Connections/h (max) 30 6 24 12 8 12 8 4 8 - 10 - 4 12 2 Driver pause Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Driver break Yes No Yes No Yes Yes Yes No Yes No Yes No No Yes Yes Kerbside parking is an estimation of all available layover parking best described by its length. Starting lines are the number of bus lines with at least one trip originating in the bus terminal. Connections are the maximum identified number of connections per hour on the route identified to be the most likely timed connection for departing bus services.

3.1.3. Interlining Interlining is the practise of combining several routes into a single operational schedule (Blais, Lamont, & Rousseau, 1990). Interlining allows scheduling to be more efficient by minimising the unproductive time of vehicle blocks through eliminating extended periods of time for buses being out of service. Vehicle blocks can be divided into two separate elements, productive time, and unproductive time. Productive time refers to the parts of the vehicle block that are dedicated for passengers. Whereas unproductive time includes the parts of the vehicle block where no passenger is supposed to be on-board the vehicle, the unproductive time is necessary for service quality (Oort, 2011). Unproductive time can be expressed as the following:

𝑡𝑢𝑛𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑒 = 𝑡 𝑎𝑦𝑜𝑣𝑒𝑟 + 𝑡𝑑𝑒𝑎𝑑ℎ𝑒𝑎𝑑𝑖𝑛𝑔 (6)

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In a conventional vehicle’s block that does not offer optimised operations through interlining of routes, each vehicle ending its route will need to wait in the layover parking for the next departure on the same line. Whereas a vehicle’s block that interlines routes allows each arriving bus to wait only for the next suitable departure even if that departure is on a different route. This scheduling technique minimises the unproductive time per round trip which in return can minimise the number of buses needed to run the same service and minimise the associated operational costs.

A simplified explanation of interlining of routes in Stockholm county can be seen in figures 3-2 and 3-3. Both figures show a bus terminal that has two turning bus lines, line 1 and line 2. Each line has a headway of forty-five minutes and both lines have circular routes, i.e. lines start and terminate in the same bus terminal. The productive time for each route is one hour, i.e. time duration where passengers can be on-board. Figure 3-2 represents vehicles’ block with no interlined routes, here it takes four vehicles to operate two circulating lines. Each vehicle will need to wait thirty minutes after each trip to preserve a time headway of forty-five minutes. Unproductive time for this setting can be described as the following:

𝑡𝑢𝑛𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑒 = (𝑁𝑣𝑒ℎ𝑖𝑐 𝑒𝑠𝑡ℎ𝑒𝑎𝑑𝑤𝑎𝑦) − 𝑡𝑟𝑢𝑛𝑛𝑖𝑛𝑔 (7)

Where

𝑡𝑢𝑛𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑒 = unproductive time per terminating trip per circulating route (time unit),

𝑁𝑣𝑒ℎ𝑖𝑐 𝑒𝑠 = number of vehicles operating each line,

𝑡ℎ𝑒𝑎𝑑𝑤𝑎𝑦 = time headway per line (time unit),

𝑡𝑟𝑢𝑛𝑛𝑖𝑛𝑔 = running time or productive line (time unit).

Figure 3:2: Vehicles' block without interlined bus lines (designated as routes in the figure)

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For a circle line the unproductive time equals layover time as the 𝑡𝑑𝑒𝑎𝑑ℎ𝑒𝑎𝑑𝑖𝑛𝑔value in equation (6) becomes zero. This means that for a certain bus terminal, the increase in the count of buses that have unproductive time simultaneously, will increase the demand for layover parking in that bus terminal. The demand for layover parking in a bus terminal for I number of circle lines and for a study period of T time units can be expressed as:

[𝐿𝑎𝑦𝑜𝑣𝑒𝑟𝑖] = {1, 𝑖𝑓 𝑡𝑢𝑛𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑒,𝑖 ∈ ℕ+ 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

𝑁 𝑒𝑟𝑡ℎ𝑠 = max({ [𝐿𝑎𝑦𝑜𝑣𝑒𝑟𝑖]𝑡 ∶ 𝑡 = 1 … 𝑇})𝑖=1

(8)

Where

𝑁 𝑒𝑟𝑡ℎ𝑠 = number of berths (loading areas) required for layover,

𝑡𝑢𝑛𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑒,𝑖 = unproductive time for line i.

The example given in Figure 3-2 suggests that there will be a total of three (6 × 30 𝑚𝑖𝑛𝑢𝑡𝑒𝑠) hours of unproductive time during the time period between 12:00 pm and 04:20 pm. The optimal layover parking capacity needed for the two bus lines that are indicated in Figure 3-2 will be two berths. The vertical dashed red line gives an example of the time of the day when two berths of layover parking are occupied, notice that the same number of two simultaneous unproductive buses repeats during different times of the day and it is the maximum value needed, same findings can be obtained when using equation (4) that is suggested by the Transit Capacity and Quality of Service Manual. And the following expression is considered to be true:

1.2 𝑡 𝑎𝑦𝑜𝑣𝑒𝑟

𝑡ℎ𝑒𝑎𝑑𝑤𝑎𝑦 = max({ [𝐿𝑎𝑦𝑜𝑣𝑒𝑟𝑖]𝑡 ∶ 𝑡 = 1 … 𝑇})

𝑖=1

(9)

Figure 3-3 represents the same bus terminal with the same number of lines and time headways as figure 3-2, but figure 3-3 incorporates vehicle blocks that interline the two routes. The figure shows that it is possible to attain the same service but with less unproductive time, here the overall unproductive time equals 50 minutes (4 ×5 𝑚𝑖𝑛𝑢𝑡𝑒𝑠 + 3 × 10 𝑚𝑖𝑛𝑢𝑡𝑒𝑠) for the time duration between 12:00 (12:00 noon) and 16:20 (04:20 pm). This decrease in unproductive time is a result of better fleet utilisation and allows for three buses to operate the two routes instead of four vehicles that were presented in figure 3-2.

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Figure 3:3: Vehicles' block with interlined routes

When using equation (8) to find the optimal layover parking capacity required for the services in figure 3-3, the optimal number of layover parking berths yields only one instead of two, since the decrease in unproductive time associated with each departure resulted in eliminating simultaneous unproductive times, this contradicts the result obtained from equation (4) in chapter 2 that is proposed by the Transit Capacity and Quality of Service Manual (2013). Indicating that equality between equation (8) for I number of buses and equation (4) no longer holds:

1.2 𝑡 𝑎𝑦𝑜𝑣𝑒𝑟

𝑡ℎ𝑒𝑎𝑑𝑤𝑎𝑦 ≠ max({ [𝐿𝑎𝑦𝑜𝑣𝑒𝑟𝑖]𝑡 ∶ 𝑡 = 1 … 𝑇})

𝑖=1

(10)

This contrast between the layover parking capacity requirement for the example in figure 3-3 and the results of the model proposed by the Transit Capacity and Quality of Service Manual in equation (4) can be explained by the existence of interlined routes in the operational schedules. Although interlining offers greater optimisation of the service, it is a complex process that results in uncertainty for the requirement of layover parking capacity in a bus terminal. Figure 3-3 was a simple example of interlining of only two routes that are circular and that terminate in the same bus terminal. Practically in Stockholm county, interlining is far more complex, and to add further room for the optimisation of bus operations, The Public Transport Administration in Stockholm County allows vehicle blocks to interline routes of different bus terminals. For example, a bus can terminate a trip on line 1 in bus terminal A, and then operate on line 2 from bus terminal B, adding further complexity in estimations for the optimal layover parking capacity required for both bus terminals, where analytical approaches to describe such system becomes difficult.

Interlining of routes within different bus terminals is a common approach for bus scheduling in Stockholm county, not only does it offer better optimisation capabilities of bus operations and the reduction of operation costs, it also minimises the footprint of bus operations in central Stockholm. Interlining can be intentionally incorporated to avoid dedicating layover parking space in an attractive urban space. This is not the case for many bus terminals in Stockholm, where interlining is inevitable as these bus

Vehicle 1

Vehicle 2Vehicle 3

12:00 12:15 12:30 12:45 13:00 13:15 13:30 13:45

Route 1

16:00 16:1515:30 15:4515:00 15:1514:30 14:4514:00 14:15

Unproductive timeRoute 2

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terminals suffer from lack of space dedicated for layover activities, and in contrast to bus terminals in central Stockholm, interlining and deadheading become operational instruments practiced by operators to mitigate the lack of space dedicated for layover, which results in increased operational cost and decreased service quality. Further discussion on coping with layover parking capacity limitations is found in section 3.4.

3.2. Bus Layover Parking in Bus Terminals This section describes bus movement within bus terminals and how layover parking capacity is induced.

3.2.1. Traffic Movements in Bus Terminals Dedicated spaces for bus movements in bus terminals in Stockholm are usually compact, thus require careful planning of traffic flows within and through the bus terminal. The planning of movements is vital for the safety, convenience, and efficiency of bus operations. In a typical bus terminal in Stockholm the following key locations can be seen (Gunnarson & Lindqvist, 1988; Stockholms läns landsting, 2019):

x Alighting stop: It is a type of bus stop that is dedicated only for disembarking

passengers. A single alighting stop can be used by one or several bus lines, and it should be placed near entrances to the rail service if present, to ensure efficient transfers between the different modes, or to be placed close to points of interest such as shopping malls.

x Boarding stop: It is a stop that is dedicated only for boarding of passengers. A single boarding stop can be used by one or several lines, but a boarding stop is usually shared with fewer lines than a disembarkation stop, particularly due to the more time consuming boarding process compared to the disembarkation process, where the duration of the first is directly associated to the type of ticketing system.

x Boarding/alighting stop: It is a type of stop that provides location for both disembarkation and boarding process together, and it can be used by one or several lines. This type of bus stop is typically used by continuing lines, i.e. lines that do not start or terminate in that bus terminal. A short buffer time for schedule recovery can be spent at these stops.

x Time buffering zone: This area is dedicated for buses with short timetable recovery time. In bus terminals where connection to rail services is provided, the time buffering zone can be used by rail service replacement buses in cases of rail service disruption, planned or unplanned.

x Layover parking zone: A layover parking is dedicated for layover activity purposes. A bus will typically occupy a layover berth for at least 10 minutes; thus, layover berths are provided for vehicles that are out of service for long time durations. Layover parking zone is commonly established within the premises of a bus terminal to ensure adequate service, but in some cases the layover parking zone can exist outside the bus terminal, but it should be at least within walking distance from the bus terminal where locations of other layover activities exist. Section 3.2.2 provides details of the layover activities in bus terminals and their facilities.

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The common flow of bus movements in bus terminals can be seen in figure 3-4. Arriving buses which will terminate in the bus terminal will disembark all passengers onboard at the disembarkation stop (indicated in red) and then head towards the layover parking zone (indicated in yellow) to park the bus and start any necessary layover activity. After layover time has ended, the bus will go to the boarding stop (indicated in green) to start a new trip by boarding the passengers waiting at that stop. To have this arrangement of bus terminal activities, it is important that the planning of the bus terminal allows for circulating traffic movements for buses without leaving the bus terminal, and that all the buses in the layover parking zone should be able to reach every boarding stop in that bus terminal (Stockholms läns landsting, 2019).

Figure 3:4:Bus circulation within a bus terminal

3.2.2. Layover Activities and Amenities The layover parking zone in a bus terminal is needed to provide a storage space for buses during their layover time when the driver or the bus undergoes layover activities by keeping inactive vehicles away from the unloading and loading areas, i.e. the bus is out of service. In Stockholm county, there are four reasons that may require the bus to be out of service and to be parked in a layover parking, some of those reasons are associated with working hours of the driver, energy consumption of the bus or based on the requirement of the timetable itself (Stockholms läns landsting, 2019). According to Stockholms läns landsting (2019), those layover activities can be divided into four categories, the list continues on the next page:

x Schedule recovery: As previously assessed in section 2.4.2, when public transport operators schedule a certain bus service, they provide slack time in the vehicle block to provide more service delay resilience and to minimise delay penalty fines. If a certain bus is not in need for the slack time to keep up with its schedule, the bus

34

will have to wait in the bus terminal until the slack time is spent before starting its next departure to avoid early departure penalty fines. This bus activity is referred to as schedule recovery. During schedule recovery, the driver remains onboard the bus and the waiting time is usually less than fifteen minutes. In some cases, when the departure stops are not utilised by other buses due to large headways, a bus on schedule recovery can spend its slack time there instead of in a layover parking berth.

x Pause: A pause is a short break provided for drivers where they are not required to be onboard the bus, and the bus should be parked in the layover parking zone. In Stockholm county, a pause consists of a minimum of ten minutes and allows the driver to stretch his or her legs and use bathroom facilities. This indicates the necessity to have the pause room in close location to the layover parking zone. The pause activity is part of an agreement between the drivers’ union and public transport operators, and it should be provided at least once every two and a half working hours (Sveriges Bussföretag & Svenska Kommunalarbetareförbundet, 2017). A pause can be replaced by a break.

x Break: Similar to the pause, a break is provided for drivers where they are not required to be onboard the bus, and the bus should be parked in the layover parking zone or as part of vehicle block optimisation be taken over by another driver (schedule recovery time can in this case be added to the vehicle block for improved service reliability). Time duration for a break should not be less than 30 minutes as it should be sufficient for the drivers to have their meal, to have access to a toilet facility and to have an opportunity to get adequate rest. The break activity is also a part of the agreement between public transport operators and the drivers’ union, where a break is mandatory at least once for every five working hours.

x Charging for electric buses: Charging for electric buses in Stockholm county can take place in two locations, either at a bus depot where charging takes between 4-6 hours through a charging cable, or at the bus terminal when the bus is parked at the layover parking zone. Charging a bus in a layover parking berth is done inductively via a pantograph attached to a ceiling or planted in the ground, where this type of charging lasts between 5-10 minutes and the driver can leave the bus during charging. In 2018, there were eight electric buses in Stockholm county (AB Storstockholms Lokaltrafik, 2019).

Amenities in a bus terminal where layover parking zone is provided should include a personnel room and space for electric transformers and their auxiliary equipment. Depending on the bus terminal, the personnel room can be established for only pause activities, or can be established for both pause and break activities. Personnel rooms which accommodate only pause activities provide access to toilet and personnel rooms dedicated for break activities provide access to toilet, microwave oven to heat food, table, and seating for drivers to rest. In some cases, space for religious worship is provided. According to the guidelines provided by Stockholms läns landsting (2019) personnel rooms should not be located further than 150 meters from the layover parking zone.

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The differentiation between pause and break amenities in a specific bus terminal has direct impact on the scheduling of bus services terminating in that bus terminal. In a bus terminal where the personnel room is only provided for pause activities, terminating bus services will not normally have as long layover time as bus terminals that accommodate break rooms, since drivers will not normally need more than 30 minutes to have a pause activity. Accordingly, bus terminals that provide access to a personnel room that accommodates break activities will have bus drivers who will spend layover time that can range from 10 minutes to more than 30 minutes, which points out that the layover parking zone will be occupied for longer durations, disregarding possible effects of fleet optimisation by replacing the driver. The layover time in typical bus terminal in Stockholm county can be expressed as the following:

𝑡 𝑎𝑦𝑜𝑣𝑒𝑟 = 𝑡𝑠 𝑎𝑐𝑘 + 𝑡 𝑟𝑒𝑎𝑘 + 𝑡𝑠𝑢𝑝𝑝 𝑒𝑚𝑒𝑛𝑡 (11)

Where

𝑡 𝑎𝑦𝑜𝑣𝑒𝑟 = total layover time (time unit),

𝑡𝑠 𝑎𝑐𝑘 = slack time dedicated for schedule recovery activity (time unit),

𝑡𝑠𝑢𝑝𝑝 𝑒𝑚𝑒𝑛𝑡 = charging time dedicated for electric buses (time unit),

𝑡 𝑟𝑒𝑎𝑘 = rest time, either for pause or break activity (time unit).

To improve operational reliability some bus terminals are equipped with a ramp providing pressurised air, 24 Volt charging and heating for inactive vehicles. In bus terminals where rail services are provided, the guidelines of Stockholms läns landsting (2019) state that spaces dedicated for rail replacement bus service should be provided, and in cases where the replacement bus lines need to terminate in that bus terminal, extra layover parking berths should be provided, in addition to the impact of time connection between rail and bus services that will be discussed in section 3.3.1, rail service replacement buses add further layover parking capacity requirement.

3.2.3. Assessment of Bus Terminals There were two previous studies on the bus terminals’ level of service on a very general level by The Public Transport Administration, conducted in 2014 and in 2015 (see appendix D). Both of the studies provided only a general grading on the level of service with no specific grading regarding layover parking capacity. The results of both studies are to a great extent overlapping and the level of service is not always consistent between the studies or within one study. This is likely a result of a very open formulation of the research question and a subjective grading system.

The grading system used for the bus terminals’ level of service in these studies, was divided in three tiers: green, yellow and red. The criteria for each tier were qualitative where a green-rated bus terminal, by a subjective perspective, was satisfactory in the layout, perceived as safe by customers, and operationally sound and well-dimensioned. A red rating on the contrary, indicated that the bus terminal was problematic, and a yellow rating indicated that the bus terminal was acceptable, all in a broader qualitative sense without distinct criteria and subjectively based on the responder’s experience.

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The responder could also add free-text comments together with their responses. Thus, a more detailed assessment of layover parking zones in bus terminals needed to be carried out in order to have a better comprehension of what is constituted as an insufficient capacity for the layover parking zone. The methodology chapter provides details about the further assessment carried out through a survey study.

3.3. Demand for Layover Parking The majority of the demand for layover parking in a certain bus terminal is a product of scheduling techniques practiced by the public transport operator and the design criteria that are incorporated during the scheduling process. While the other small share can be seen as an induced demand from capacity limitations in other bus terminals in the network. Section 3.1.3 discussed that when interlining techniques are incorporated in the planning process of bus schedules, demand predictions for the layover parking becomes complex, and that lines headways and traffic volumes alone do not correlate to the actual need for a layover parking.

While searching for solutions from public transport agencies from different countries other than Sweden, it was evident that the forecasting issue of future needs of layover parking capacity during the planning stages is an international problem. Some agencies reported using simplified analytical models of which the authors presented the limitations that exist in such tools and that the complications of such forecasts will not be observable until later during operations. Other countries on the other hand, have used an approach of informal “trial and error” through computer-based optimisation tools of bus and crew schedules such as Giro Hastus.

This informal yet appropriate approach is practiced by bus traffic planners during the planning stages of a bus terminal through specifying different layover parking capacity into Giro Hastus, and observing the resulting operations cost associated with each setting and iterating until a low satisfactory cost value has been reached. The issue with this method is that it is time consuming to obtain a satisfactory result, it does not reveal clear correlations between different variables and that there is uncertainty that the chosen layover parking capacity is actually the optimum practical choice.

In this section, a study is carried out to identify which variables and attributes shape the demand for layover parking in a certain bus terminal, and how public transport operators cope with layover capacity limitations. As the goal of this thesis is to recommend a strategic tool that can be used during the planning stages of a bus terminal and not later during operations, this investigation is only focused on network, bus terminal and service properties that are also available during the planning stages. Properties that are determined at later stages are disregarded since these are unknown in the bus terminal planning process, such as the exact timetables of bus services in the bus terminal. Variables that form demand for layover parking in a bus terminal can be classified into two groups: service variables and network attributes.

Service variables include the effect of the total number of terminating lines, the effect of lines’ headways and the effect of rail services, and network attributes include the

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effect of personnel room and the effect of bus terminal’s centrality within the bus network. To illustrate the impact of different variables and attributes on the capacity needs for layover parking in a bus terminal, a series of illustrative examples comparable with vehicle blocks will be presented. These graphs illustrate time-based activities inside the bus terminal on a bus line basis and not on a bus basis such as those of an actual vehicle block.

Figure 3-5 shows bus terminal activities for six lines, where four of them are continuing lines and the other two are turning ones. One should keep in mind that those figures are not true vehicle blocks and each departure on a certain line could be performed by a different bus. Line headways are indicated under the line number in the graph. Also, figures 3-5 to 3-9 show layover parking needs that are a product of the scheduling techniques practiced by operators, and it neglects layover demand induced by capacity limitations from other bus terminals in the network. The latter will be discussed in the network attributes section 3.3.2.

For terminating lines, figure 3-5 shows different layover activities for buses before their departures. For example, line 2 has a headway of 30 minutes between consecutive departures, the bus departing at 12:30 has layover activity of a pause type for a duration of fifteen minutes and has two slack time durations of three and two minutes, respectively. The first slack time is added for driving time while the second slack time is added for the layover time. A different type of layover activity can be seen on the second bus on line 2 that departs at 13:00, here the bus only has schedule recovery activity, indicating the bus driver is expected to remain onboard during that time. A larger layover time can be seen on line 1, where the bus that departs at 12:50 has a layover activity of break type for a duration of forty minutes, and two slack times added of three and one minutes, respectively. In this setup, a total of two layover parking berths will be needed in this bus terminal, due to the buses on lines 1 and 2 for the shaded periods between 12:10 to 12:30 and 13:40 to 13:50.

Figure 3:5: Graphical presentation of bus terminal activities for different lines.

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3.3.1. Service Variables This section covers the impact of service-related variables such as headway or timed connections on layover parking requirement.

The Effect of Turning Lines Since layover activities do not take place for in-service buses, layover parking in bus terminals would only be occupied by buses of turning, starting, or terminating trips. It would be logical to predict that with increased number of terminating lines in a bus terminal the demand for layover parking zone will be increased. Figure 3-6 shows a comparison between base and adjusted scenarios.

Figure 3:6: Comparison between base scenario and a scenario adjusted for terminating lines

The base scenario uses the same setting that was previously presented, while the adjusted scenario converts line 5 from a continuing line into a turning line where layover activities are incorporated. The adjusted scenario shows that the demand for layover parking zone changed from two layover berths into three. The three berths will be required during the intervals 12:10 (12:10 pm) to 12:25 (12:25 pm) and 13:40 (01:40 pm) to 13:50 (01:50 pm). It becomes clear that there is a positive correlation between turning bus services and the need for layover parking capacity.

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The Effect of Headways The effect of departures headways for turning lines can be apparent on the capacity requirements for a layover parking zone in a bus terminal, where the more frequent the service is, the more demanding a line can be for layover spaces. Figure 3-7 shows a comparison between a base scenario and an adjusted scenario where the departures headway for line 1 has been changed from 60 minutes per departure to 30 minutes. For line 1 in the adjusted scenario, there are two intervals where layover activities can happen simultaneously for several buses of a same line. Looking at line 1 in the adjusted scenario, one can notice that there are two buses having their layover time from 12:06 to 12:20, whereas in the base scenario, line 1 had only one bus for that time period. Considering the layover time spent for buses in line 2 as well, the total requirement for the layover parking zone in the adjusted scenario becomes three.

The Effect of Timed Connections Many major bus terminals in Stockholm county are connected to rail services such as the underground metro services and commuter train services. Most bus lines in these bus terminals shall provide time connections with the rail services according to agreements between the public bus operators and the Public Transport Administration. Timed connections are provided to allow for passengers to have efficient transfers and provide a better overall multimodal travel experience by providing bus departures and bus arrivals within a time interval that allows for transfers between platforms of train services and the bus stops. Figure 3:8: Effect of connections on layover

requirements.

Figure 3:7: Effect of headways on layover requirements

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According to this, it is expected that for bus terminals where time connections are provided by several bus lines, it becomes more probable that bus operators will incorporate layover activities before the specified bus departures to ensure synchronisation between bus and train services. Thus, these lines will generate a higher demand for layover parking capacity as multiple buses will need to spend layover time simultaneously in the bus terminal. Figure 3-8 provides an illustration through a comparison between two scenarios. The base scenario presents a bus terminal with no rail services connections, three bus lines out of six are turning lines where there is no relation between their departures except for periodic headways, the maximum layover capacity requirement is two layover berths for that period. The adjusted scenario presents the same bus terminal with the same departure headways for all of its lines, but with lines 1, 2 and 4 synchronised to a rail service running once every hour. The adjusted scenario shows that layover activities for these lines take place simultaneously which increases the demand for layover parking capacity, and the maximum requirement for layover parking increases to three.

The Effect of Legal Agreements The bus service contracts in Stockholm county permit each public transport operator to use layover amenities that are established in its contract area in the way that the operator finds feasible to lower operational costs and improve the provided service.

These contract areas are part of the procurement process that public transport operators undergo to ensure fair competition. An operator can bid for several contract areas with no requirements for the adjacency of the areas. This implies that an operator can win several contract areas that are adjacent, and it is possible for the operator to further optimise its bus operation according to the resources available in all of the contract areas that were won. This opportunity does not exist in the same manner for operators that operate in a geographically separated contract areas as synergies of coordinating operations are less likely in the latter case. Adjacent contract areas that are available for a single operator add more flexibility in the bus scheduling process, it allows the operator to explore synergies to better optimise utilisation for layover parking zones and depots. This flexibility can impact the requirement for layover parking capacity, and its effect is asserted in the methodology chapter, section 4.2.2.

Another legal agreement that can be considered as a factor impacting the required layover parking capacity of a bus terminal is the collective agreement between Kommunalarbetareförbundet (the union of the drivers) and Bussarbetsgivarna (the trade association of the bus operators). Section 3.2.2 discussed that part of the union’s agreement that no bus driver should drive more than two and a half continuous hours without a pause of at least ten minutes, and that no bus driver should work more than five hours without a break of at least thirty minutes. These regulations are to ensure safe operations by reducing drivers’ fatigue and providing a good working environment. These regulations have an impact on the bus scheduling as public bus operators need to incorporate these regulations as a criterion in their scheduling process, which will ultimately impact the capacity requirement for layover parking in

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bus terminals. For instance, if a certain line has a total driving time of 120 minutes in each direction, and the bus driver who will start driving in the outbound direction has completed a pause activity twenty-five minutes before commencing that trip, the probability that the driver will require to have another pause activity at the destination becomes one-hundred percent. The effect of the total driving time according to the current union’s agreement will be assessed in the methodology chapter.

3.3.2. Network Attributes In this section factors relating to network attributes will be explored, such as provided facilities and the location where they are provided.

The Effect of Personnel Room The presence and the type of a personnel room that is established in a bus terminal is a defining factor for the layover parking capacity required in a bus terminal. A bus terminal that has relatively higher bus service intensity but without access to facilities enabling driver pause can therefore have a lower requirement for layover parking capacity than a bus terminal with relatively less service intensity but with facilities enabling driver pauses. Section 3.2.2 stated that different layover activities that can happen during layover time, two of those activities, namely pause and break activities, require that the bus terminal provides access to a personnel room. If access to a personnel room is not provided the only layover activity that can be done for a turning line will be schedule recovery, which in many cases can be done at the departure stops, eliminating the need for a dedicated layover parking zone.

Moreover, section 3.2.2 discussed that if a bus terminal provides access to a personnel room which does not have a microwave oven for meal heating, that personnel room cannot be used for driver break activities as drivers cannot have their meals there. Thus, layover activities in that bus terminal would be restricted to pause and schedule recovery and it is highly probable that these activities will last for a duration of less than half an hour. Finally, if a bus terminal provides access to a

Figure 3:9: Effect of personnel room on layover requirements

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personnel room which allows for driver breaks, that bus terminal can support all possible layover activities: schedule recovery, pause and break. This can be assumed to increase the average layover time in a bus terminal compared to a bus terminal with less facilities for layover activities if all other factors remain identical.

Figure 3-9 presents three different scenarios for bus terminals with identical bus service offering for passengers. In the base scenario, the bus terminal does not have facilities for break nor pause. Adjusted scenario 1 presents a possible scenario where driver pause is enabled. Adjusted scenario 2 presents a possible scenario where both driver pause and driver break are enabled. In the base scenario all bus departures can only be preceded with schedule recovery that may be performed at a departure bus stop, other layover activities cannot take place due to the absence of a personnel room, thus no layover parking zone has to be provided, given that buses can use the departure bus stop for schedule recovery. In cases where several bus lines share the same departure stop, and their schedule recovery time is larger than departure headways, a layover parking berth should be provided. In the adjusted scenario 1, buses can have a pause activity, indicating that some buses may need to layover for at least ten minutes but likely less than thirty minutes for pauses, in this example this has created a need for a layover parking capacity of at least two berths. Finally, the adjusted scenario 2 provides an example where the provided break-enabling facilities have induced a requirement for additional layover parking capacity, with some vehicles possibly laying over for at least half an hour. These extended durations of layover time can create further requirements for the layover parking capacity, as in this scenario the required number of layover parking berths has increased from two to three, compared to adjusted scenario 1.

The Effect of Centrality Using the definition of a bus terminal from section 3.1.2, Stockholm county enfolds a public bus network of sixty-one bus terminals. These bus terminals can be seen as nodes in a network connected with links of public bus lines and links of routes used for deadheading from one bus terminal to another. The spatial localisation of a bus terminal within this network could possibly have an effect on the layover parking requirement in the bus terminal. Among demand generated by scheduling techniques practiced by operators, a layover parking demand in a specific bus terminal can be induced by capacity limitations in other bus terminals in the network. In certain cases, when deadheading is incorporated in a bus block between two bus terminals, a bus driver may have a pause or a break activity before reaching the destination bus terminal due to operational optimisations or the driving time limitations that are enforced by the union’s agreement, and that pause or break may be done in a bus terminal other than the destination or origin bus terminals. This indicates that bus terminals which have higher number of links (deadheading routes) may possibly experience an increased number of layover activities compared to bus terminals that are not well connected by links of deadheading routes. This phenomenon is associated with best practices used by operators to cope with layover parking capacity limitations in bus terminals that is presented in section 3.4.

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3.4. Practical Adaptation Techniques

The literature review discussed that insufficient layover parking capacity in a bus terminal may jeopardise bus service reliability. Layover capacity underestimation during planning stages of bus terminals limits the opportunities for public transport operators to provide a reliable service and it imposes extra costs on operators during operation stages to maintain a certain level of service reliability. Niels van Oort (2011) provided a detailed study on service reliability in theoretical and practical perspectives. His study showed that unreliability of a service is a product of a mismatch between planning and operations, and to improve service reliability a better match between them should be brought closer, either by adjusting operations towards planning or by adjusting planning towards operations as indicated in figure 3-10. The study discussed a variety of instruments and design choices on planning, tactical and operational levels.

Figure 3:10: Two opposing ways of improving service reliability (source Oort, 2011)

Instruments can be classified into two categories, preventive and responsive (remedying). Preventive instruments avert service unreliability from arising later during operations, table 2-1 in section 2.2 presented examples of preventive instruments such as providing adequate capacity of bus terminal facilities during planning stages, where those preventive instruments when implemented can lower the need for operational instruments to maintain a certain level of service reliability. On the other hand, the majority of instruments applied at operational levels are of responsive type, where interventions are needed to balance disturbances that already have occurred in order to “remedy” the service reliability.

When applying operational instruments due to suboptimal design choices at the strategic level there is an operational disadvantage. This means that operations can be more expensive, have an increased negative environmental impact and be less resilient to service abnormalities compared to an optimal situation (Cham, 2006; Oort, 2011). Niels van Oort (2011) provided a detailed discussion on responsive instruments, including stop-skipping, deadheading, short turning and vehicle holding.

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In the context of this study, and considering the existing bus terminals in Stockholm county, the layover parking capacity is to be considered a strategic design choice that is not available for adjustments for an existing infrastructure. To provide the appropriate match between planning and operations, operational and tactical instruments must be adopted by public transport operators to adjust operations towards planning to create a better match that is needed to provide the required level of reliability of the service. Identifying the appropriate operational instruments and the extent of their usage by public transport operators to cope with capacity limitations is a key step in this thesis’s framework, where an extensive implementation of such operational instruments to cope with layover parking capacity limitations can be interpreted as a measure for the inadequacy of the provided layover parking capacity.

The literature review revealed a shortfall in publicly available knowledge on how public bus operators can adapt to insufficient layover parking capacity, and what appropriate operational and tactical instruments can be implemented to overcome these limitations. It was evident that knowledge on the practical aspects of managing layover parking capacity constraints was considered sensitive by the operators, and different operators may handle the issue with different degrees of usage of different operational and tactical instruments. In order to identify the adopted instruments, a series of interviews with different personnel from different public transport operators, public transport authorities and transport consultants were conducted along with site visits and a questionnaire. Two main instruments were identified: deadheading and change of drivers.

In general, and with disregard to coping with insufficient layover parking capacity, deadheading is a common operational procedure that allows the service to be more demand responsive. Deadheading is incorporated in the operational schedule of a bus to allow the bus to speed up by cancelling all possible dwelling times along its route until the bus reaches its destination where it can be on-time for its next departure (Oort, 2011). If passenger demand for bus service is mainly unidirectional, then deadheading is an efficient way of optimising fleet utilisation and to preserve on time performance, compared to offering the service fully omnidirectional. When interlining is incorporated in scheduling, the instrument of deadheading can be executed upon arrival to the last stop in a certain bus terminal to relocate the bus to another bus terminal where it will initiate a new line as discussed in section 3.1.3.

In Stockholm county, where interlining is permitted, deadheading can take place either on a single direction of a line, or for the bus to move to another bus terminal to start a new line. Deadheading on a single direction of a line usually happens during morning rush hours to connect passengers from residential areas to business areas, or the other way around during evening or afternoon rush hours. Layover activities are usually needed in the hours preceding or succeeding rush hours, since during rush hours public bus operators maximise fleet utilisation and they schedule their fleet in a way that maximises their productive time to match passenger demands, and their need for a layover parking during peak hours is decreased.

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To illustrate this, four simplified examples can be used. The following examples do not account for drivers’ working-time optimisation (which is outside the scope of this study). To account for layover activities when interlining is applied, in an ideal situation where layover parking capacity limitations are absent, it is more likely that a bus will spend layover time as close as possible to the starting point of the next assigned revenue service where deadheading has ended (destination bus terminal), since the bus and the driver can have layover activities very close to the departure stop, prompting the service to be reliable and lowering the risks of late departures. This scenario is presented in column 1 of figure 3-11. The same applies if layover parking capacity were insufficient at the starting point of the deadheading (origin bus terminal) whereas only the starting point of the next assigned revenue service (destination bus terminal) has sufficient layover parking capacity. This scenario is presented in column 2 of figure 3-11. In both scenario 1 and 2, layover parking capacity was sufficient, and it did not have any direct effect on typical bus operations with permitted interlining, where public bus operators did not need to use extra instruments as planning and operations are in match, specifically in the destination bus terminal.

Figure 3:11: Possible choices of layover locations

The scenarios 3 and 4 in figure 3-11 are examples of when extra interventions are needed from public transport operators to adopt to the layover parking capacity insufficiency. Column 3 presents a scenario where the starting point for deadheading (origin bus terminal) have unconstrained layover parking capacity and a starting point for the next assigned revenue service (destination bus terminal) with insufficient layover capacity. In this scenario a layover is required before arriving to the vicinity of the starting point of the next assigned revenue service (destination bus terminal) due to layover capacity limitations in that bus terminal.

Depending on the precision of travel time estimation for the deadheading, this may decrease service punctuality. If the operator is forced to locate layover in locations suboptimal to planning out of operational constraints this leads to the operator being forced to take unwanted risks of delay penalties, essentially leading to higher operational costs and less service resilience to disruption. Scenario four presents the least optimal scenario where the operator is forced to compromise the operational performance, given the condition to disregard driver working time optimisation. In the

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case of capacity constraints, the operator also has the obvious alternative of splitting layover time between several locations. In this case driver pause or break can be set in one location and timetable recovery time can be planned at the location of the next assigned revenue service. This setup is also to be considered a compromise, risking suboptimal resilience against service abnormalities.

Considering the association between slack times and operational costs that was presented in section 2.4.2 of the literature review, the capacity limitation in the destination bus terminal is a product of underestimations in the planning stages, and although service can still be reliable due to slack times added, better planning would have enabled more cost-efficient operations and eliminated the need for the additional tactical instrument of additional slack times. To cope with layover parking capacity limitations in the destination bus terminals in both scenarios 3 and 4, the public transport operator needs to identify an alternative location rather than the destination bus terminal, the process of identifying possible alternative locations for layover activities should satisfy several criteria:

x The chosen alternative layover location must have adequate amenities not less than what is agreed upon with the drivers’ union,

x The chosen alternative should be the alternative with the minimum associated operational costs, i.e. not too far from the shortest path between the origin and destination bus terminal as this would induce more travel time variability that requires longer slack time and increase vehicle mileage (leading to higher maintenance cost, increased energy use, and increased environmental impact),

x The alternative location must not jeopardise service reliability and on-time performance (may require additional slack time)

Figure 3-11 suggests that in scenario 3, the bus can be scheduled to have layover where the previous revenue service terminated (origin bus terminal), before deadheading to the starting point of the next assigned revenue service (destination bus terminal). This alternative location is only available for scenario 3 and not for scenario 4 since the location where the previous revenue service terminated in scenario 4 does not have available layover parking capacity. A drawback of this alternative is the potentially long distance between the terminus of the previous revenue service and the starting point of the next assigned revenue service, and the associated travel time variability that can be accompanied with the deadheading towards the destination bus terminal and the additional slack times.

Public transport bus operators may compensate for this issue during the operational scheduling process by incorporating more interlining between distance-close bus terminals to lower the distance of deadheading. Another possible solution for layover that can be selected in both scenario 3 and 4 is to use a third location with available layover parking capacity and adequate layover facilities. This may have a negative impact on service reliability and lead to an increase in travel distance and time, leading to a less than optimal solution with possibly increased operational cost and environmental impact.

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The same drawbacks can exist with another alternative layover parking location, such as laying over in an empty land space nearby to either the destination or the origin bus terminal. Some public operators reported that they sometimes schedule the bus to be parked in a space that is not part of the bus terminal and the bus driver can still reach the personnel room within walking distance, such as on a sideroad under a bridge where buses use it as a path to enter or exit the bus terminal, without effecting buses accessibility to the bus terminal.

Another instrument used by public bus operators to cope with layover parking capacity limitations in the destination bus terminal is the change of driver. Changing the driver is a tactical instrument that is frequently used during peak hours to maximise the productive time of the vehicle and decrease deadheading to match larger passenger demands during these hours. Changing of drivers can also be utilised as an operational tool when layover parking capacity is insufficient in a bus terminal. The operator can decrease the time a vehicle spends in a bus terminal by planning a change of driver during driver pause or break, thus decreasing the layover parking requirement. Change of driver can also be done at any bus stop along the road. Changing of the driver of a certain bus separates the demand for layover for driver rest from other operationally required layover time. The change of driver instrument is less useful for electrical vehicles requiring layover time for charging.

Changing the driver adds further complexity in the operational plan, and it requires that a second driver needs to be ready at the right place in the right time to maintain punctuality. The adherence of the driver to the operational plan is of importance to the successfulness of this instrument. Although adding small slack time to the dwell time of the bus at the stop can add time flexibility for changing roles, this might extend the journey time and reduces customer satisfaction for the passengers onboard. There are two possible drawbacks that might take place with this instrument. First, it is possible that the new driver is delayed, causing reliability issues. Second, if there were several driver changes planned to take place simultaneously for several lines at the same facility, it may be possible due to human error that new drivers swap with the wrong colleague, causing service abnormalities, including cancellations. In the case of driver changes at bus stops along a route there is also an increased risk of service disturbance and possible amplification of abnormalities when the other driver is delayed, due to the personal hand-over of the vehicle with passengers onboard.

3.5. Available Resources and Limitations According to the discussions provided in section 3.4, the extent of usage of certain operational and tactical instruments adopted by public transport operators can reveal how inadequate the provided capacity of the layover parking zone in a bus terminal is. A large barrier faced during this thesis study was acquiring information about these instruments. Public transport operators consider any data related to activities’ information during unproductive times to be business sensitive information. The reason for this is that planning of activities executed during unproductive times comprises a compilation of information about practices, instruments and patters that confers economic value and benefit to the public bus operator.

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A comprehensive analysis of such confidential data could produce an estimation of the costs associated with an operator’s operations. An accurate estimation of operational costs of a certain operator could help other rival operators in estimating future bids’ prices of that operator. Moreover, during the years this thesis study was conducted (2019-2020), there were several ongoing tendering processes for bus services in the Stockholm region, leading to the operators being less willing to share potentially business sensitive information without requiring a non-disclosure agreement, which would have been against the ambition of providing the results freely and without consideration of commercial interests.

Information and data related to operations during unproductive times include deadheading information such as the origin and destination bus terminals, time durations of deadheading and routes. Also, activities during unproductive time include information about layover activities, such as the chosen layover parking location, the time durations of layover activities and time durations of schedule recovery which includes slack times incorporated by the operator. All this information is considered confidential. Data resources that were available for this study were only those that are open to the general public such as: bus terminal and bus depots locations and bus schedules that are available for passengers. The methodology chapter provides details on how such data confidentiality effected endorsed methods, and what tools were adopted to overcome these resource limitations.

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4. METHODOLOGY

This chapter comprises details of the exploratory research conducted with the theories presented in the case study in chapter 3. The majority of the possible variables that may have an impact on the optimum layover parking capacity were presented in section 3.3. However, these were bounded by business-sensitivity limitations which were discussed in section 3.5. In this chapter, the candidate variables and attributes are evaluated through different data sources that are open, to some extent, to the general public. The explored data sources were:

x Site visits, x Questionnaires sent to public transport operators, x Timetables for all public bus departures in Sweden obtained from Trafiklab, x Cloud computing services from Google.

The questionnaire, together with the timetables from Trafiklab were vital in assessing variables related to the bus service without the need to access business-sensitive data, while Google Cloud services were used to examine the effect of bus terminal’s centrality on the layover parking requirements.

4.1. Data Collection

This section describes the data sources of this study and how data were retrieved.

4.1.1. The Survey As discussed in section 3.5, the theories presented in the case study were based on business-sensitive data that was difficult to obtain. In an ideal case where these data could be freely accessed, the framework of this project would have included an analysis of vehicles operations during unproductive time, to identify which bus terminals had an excessive usage of deadheading and optimally also change of driver instruments and which bus terminals had normal (including defining “normal”) usage of these operational and tactical instruments. Correlating the rate of usage of these instruments to adapt with layover parking capacity constraints with the actual layover parking capacity provided could possibly have revealed the relationship between service variables/network attributes and the required layover parking capacity for different levels of service. Unfortunately, the described data were not available for study and further interventions were needed.

In general, experienced traffic planners who work on the operational scheduling do have a good sense of identifying which bus terminals need excessive usage of operational and tactical instruments to adopt to the layover parking capacity limitations and which bus terminals have sufficient layover parking capacity. To avoid the need to directly accessing sensitive data sources, questionnaires were directed to experienced traffic planners of each public transport bus operator of Stockholm working on operational scheduling. They were asked to identify which bus terminals need excessive tactical and operational instruments to adopt to the layover parking

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capacity provided in the bus terminal, or in other words, which bus terminals had insufficient layover parking capacity relative to the service intensity and the provided layover facilities enabling driver pauses or breaks. The conducted survey aimed to identify the extent of usage of operational and tactical instruments to adopt to the capacity limitations in the layover parking in a bus terminal without dealing with business sensitive issues.

The questionnaire was developed with a general and wide perspective, in a way that was supposed to not upset the respondent about the potential sensitivity of the question and but at the same time provided an answer related to the extra operational/tactical instruments needed for a certain bus terminal. Section 3.2.3 provided a discussion on the limitations that exist in the current assessment of level of service in bus terminals in general and specifically regarding layover parking capacity. Thus, the survey study was designed to make a better assessment of the current situation regarding the layover parking capacity in the bus terminals.

The essential network characteristics for determining the requirement of layover parking capacity were the location of where driver pause, and driver break was possible. This included bus terminals, depots, and other suitable facilities. The locations of these facilities are not secret but can be considered business sensitive when aggregated. However, these data were deemed essential for the study and since the facilities are observable using field studies, it was deemed acceptable for questions regarding the facilities to be included.

Figure 4-1 and figure 4-2 show a translated version of the questionnaire developed for this study while the original questionnaire (in Swedish) can be found in Appendix B, which was sent to the public bus operators in the Stockholm region: Arriva, Keolis, Nobina and Transdev.

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Figure 4:1: Questionnaire part A, information about layover places other than ones within the bus terminal

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Figure 4:2: Questionnaire part B, information about the sufficiency of the layover parking capacity

Additional break or pause facilities are located at the following locations (list all in use, see example):

Location Served bus stops BBA-break

BBA-pause

Comment (Driver swap at location? Usage restrictions?)

Name of location, e.g. a bus depot.

List of bus stop names relevant in the vicinity of the bus depot. E.g. bus stops for driver swap or termini with a short deadheading to the bus depot.

X X Describe if the location is used for driver swap and for what purpose (break? Pause?)

Name of location, e.g. a terminus with toilet suitable for BBA-pause.

Name of bus stop. X Describe if there area any restrictions for use or if you prefer not to use it for whatever reason and why.

Name of location, e.g. a bus terminal

Name of nearby termini which due to operational reasons induces a requirement of pause/break in the bus terminal.

X Describe if this only applies a certain variant of a line due to operational reasons, such as that specific line variant lacks pause facility at the other terminus.

Please take care and fill in all locations, even if there are not enough lines in the form. It is important that no facility is left out.

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Figure 4-2 shows the second part of the questionnaire, which was dedicated to gather information about facilities used for driver pause and driver break outside of bus terminals. The intent was to identify possible locations for public bus operators to plan their layover activities including pause and break activities. By identifying key facilities for bus operations outside of the bus terminals it was possible to better analyse the requirement of layover parking capacity, more specifically the relationship between service intensity and layover parking demand. The method for analysis of this relationship is described in section 4.2.1, sub-section “Handling driver pause and driver break”.

Figure 4-1 shows the first part of the questionnaire, which was dedicated for qualitative assessment of bus terminals. As discussed in section 3.4, it was vital to identify which bus terminals offer enough layover parking capacity so that the daily bus operations do not require excessive tactical and operational instruments and interventions to handle the required service volume without being over-dimensioned. The survey included a four-grade scale for layover parking capacity assessment, described in table 4-1.

Table 4-1: Classification of bus terminals in the survey study and their descriptions. Grade Description Rationale

Red

Layover parking capacity constraints require excessive use of responsive instruments to enable current service intensity level

Failed bus terminals where service quality and operational cost are less than optimal. A model shall describe a higher layover parking requirement than available capacity

Orange

Layover parking capacity constrains do not require excessive use of responsive instruments to enable current service intensity level, but no further growth can be accommodated without excessive use of such instruments

Operationally optimal bus terminal in respect to layover parking capacity. Suitable to build a model on these bus terminals since they operate on their true layover parking capacity

Yellow

Layover parking capacity is not constrained with current service intensity and the bus terminal can accommodate some further growth

Operationally sound bus terminals which are useful for model validation where the requirement for layover parking capacity should optimally be slightly below available capacity. Likely to be used to relieve bus terminals marked as red

Green Layover parking capacity is not a constraining factor

Currently over dimensioned bus terminal. A model shall describe a lower layover parking requirement than available capacity. Likely to be used to relieve bus terminals marked as red

A red-rated bus terminal implied that the bus traffic planner needs to incorporate additional operational or tactical instruments to adopt to the layover parking capacity limitations, according to what has been described in section 3.4. An orange-rated bus terminal implied that bus traffic planners did not need to incorporate additional instruments in the vehicles’ schedule to cope with capacity limitations, unless if bus service intensity increased. An orange-rated bus terminal represented a bus terminal with the optimum layover parking capacity, since this category of bus terminals offered a balance between a good level of quality for bus services and minimum land

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acquisition dedicated for the layover parking zone. Yellow-rated bus terminals implied that no additional instruments in the vehicles’ schedule were needed if service intensity would slightly increase whereas green-rated bus terminals could accommodate even larger increments in service intensity.

4.1.2. On-site Research Field visits were done on several occasions during this study, especially during the development of the theories presented in chapter 3. Also, in combination with Google Maps services, field visits to several bus terminals were conducted to collect information related to the design of the bus terminal such as the current number of layover parking berths, the number of departure and arrival bus stops in the bus terminal. To determine the number of layover parking spaces in an existing bus terminal the following process was used:

x Count layover berths if any, x Estimate length of any area that can be used for layover parking, could for

example be along a perimeter fence or disused bus stops, x Check if any other area is used for layover parking without restricting bus

movements within the bus terminal

The length of the buses used for public transport in the Greater Stockholm area vary typically between 12 and 18.7 metres. With buffer zones to allow for independent movement of vehicles the average length requirement for layover parking of a bus was assumed to be twenty metres for kerbside parking. The resulting number of layover parking berths will be referred to as “effective layover parking berths”.

4.1.3. Timetables and Scheduled Bus Departures The timetable data used in this study was retrieved from Samtrafiken Trafiklab (2019). It was formatted according to the GTFS (General Transit Feed Specification) developed by Google and was machine readable and covered all of Sweden, including commercial services such as Trosabussen and Flixbus.

4.1.4. Google Cloud and The Distance Matrix Google Cloud Platform is a combination of cloud computing services offered to registered users who will have access to the same infrastructure that Google uses internally for its services such as Google Maps, YouTube, and Gmail. In this project, to identify the effect of location of a bus terminal within a network of bus terminals as nodes and routes as links on the required layover parking capacity needed in that bus terminal, registration was made on one of the API platforms that Google Cloud provides, the Google Maps Platform. The Google Maps Platform provides APIs for Google Maps, such as routes, travel times, travel distances, etcetera. As described in section 3.3.2, an increased centrality of a bus terminal can attract or induce more demand from other bus terminals that may be operating below their required layover parking capacity. To have a better evaluation of this variable, API for Google Maps was accessed via a Python script that retrieved two variables for every pair of bus terminals or depots, shortest travel distance and shortest travel time by a bus or car.

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4.2. Data Analysis

By comparing timetable data and bus terminal characteristics against a level of service rating for each bus terminal and processing using regression analysis, a best fit model can be developed.

To develop candidate variables for the regression analysis part the timetable was processed into a large number of averages for each bus terminal, each describing some aspect of the bus services in the studied bus terminal. Bus terminals were selected for the study based on these criteria:

x Serviced by SL-branded bus services x Orange-rated in the survey (section 4.1.1) x In active use x Availability of data

The timetable used in this study was acquired from Samtrafiken Trafiklab (section 4.1.3) and was valid on 2019-03-27, a typical winter Wednesday with no school holidays or other significant service alterations.

4.2.1. Operational variables and regression analysis This section describes how data derived from the timetable was compared to the level of service rating of each bus terminal described in appendix A and analysed for correlations using means of regression analysis.

Variables from the Timetable An assumption was made that the requirement for layover parking also was related to variables that could be derived from the timetable, like peak service intensity. The time of day with the highest requirement for layover parking did not have to be the same as the time for highest service intensity but it was another possible way of describing the operational size of the bus terminal. To test this assumption, five variables were derived from the timetable:

1. Timetable peak sum of starting trips during a time interval.

2. Timetable peak sum of terminating trips during a time interval.

3. Timetable peak sum of runtime of starting trips during a time interval.

4. Timetable peak sum of runtime of terminating trips during a time interval.

5. Timetable peak of pulses per hour due to a timed connection for unimodal or intermodal travel.

For variables 1-4 peak intervals of 15, 30, 60, 120, 180 and 240 minutes were tested using two different definitions of peak: the interval with the highest number of trips and the interval with the highest sum of runtime of the trips during that interval.

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Handling Driver Pause and Driver Break From the survey results two variables were developed to describe the relative likelihood for a driver pause or a driver break connected to a trip, respectively. The two variables were derived from information regarding the availability of facilities for the corresponding layover activities in relation to the trip (section 4.1.1).

The effects of interlining were in the first step assumed to be equally distributed throughout the system, leading to the average need for layover parking being unaffected. The variables describing relative likelihood of utilisation of driver pause or driver break facilities, respectively, were constructed to vary between zero and two for each trip at a studied bus terminal. If there were facilities available in both ends of the trip, the corresponding variable was set to one, and if no facilities were being offered at the other end of the route, it was assumed to be double the likelihood of requiring the use of such facilities in a studied bus terminal compared to if said facilities were available in both ends of the trip, leading to the corresponding variable being set to two. If the bus terminal did not provide facilities for the activity the equivalent variable was set to zero. Table 4-2 below shows different variable values for these different scenarios. The arithmetic average of the pause factor per trip was then used as an indicator for the bus terminal as a whole.

Table 4-2: Pause and break factor values per trip. No facility in the

studied bus terminal Facility in studied bus terminal, no facility in far end of the trip

Facilities in both ends of the trip

Pause factor 0 2 1 Break factor 0 2 1

If the need for driver pause or break was compensated by replacing the driver along the route and outside endpoint bus terminals, the variable was multiplied by a set factor. In this study this factor for driver change for pause was decided to be set to one, meaning no driver change took place. The factor for break was decided to be set to 7/8 = 0.875 for testing. It was not verified by this study if these factors (1.00 and 0.875) were the optimal values (section 6.3).

These variables’ averages were assumed to be possible moderators during the selected peak period through multiplication by the variables numbered 1-4 from the timetable (see subsection “Variables from the timetable” above) and then evaluated as candidates for selection by regression analysis. Details of the moderation analysis is presented in section 5.3.1.

Regression These variables were manipulated using logarithm transformation, exponentiation transformation, dummy variables and combinations of each other to analyse possible interactions between variables within the same peak interval and the bus terminal facility variables described in sub-section “Handling driver pause and driver break” above. The data were compiled using a Python script into Microsoft Excel format and all regression analysis was done using Microsoft Excel and IBM SPSS Statistics

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software. Running a regression analysis on all possible combination of derived variables (in total more than three thousand variables including transformations) was deemed unfeasible given the limited computing power available. Therefore, a manual stepwise method was selected, where selected variables from different groups such as arriving trips, departing trips, different peak definitions etcetera were compared to each other and the better set was taken to the next step. This reduced the number of variables to slightly more than three hundred, all with the following common denominators:

1. Only departures originating in the studied bus terminal 2. Peak fifteen minutes calculated as highest sum of departing runtime 3. The dependent variable is the natural logarithm of required layover parking

requirement

The bus terminals rated orange by the bus operators (section 4.1.1) regarding layover parking were used as dependent variable and multiple regression was then tried on all possible combinations of the possible independent interaction terms with up to three variables.

The process of eliminating the first batch of regression models was qualitative: evaluating the models starting with the model with the highest adjusted R2 for correlation with the estimated bus terminal layover parking capacity (section 4.1.2). The bus terminals that were rated red or yellow by the bus operators were used to evaluate the models due to the limited set of orange-rated bus terminals available.

To narrow the selection of proposed models each was plotted using Microsoft Excel with all studied bus terminals; estimated bus terminal layover parking capacity versus modelled requirement of bus terminal layover parking capacity. Evaluation was done by visually recognising the models where bus terminals rated red in the highest possible degree were modelled to require a higher layover parking capacity than what was estimated (section 4.1.2) while keeping correlation between variables as low as possible and residuals as close to normal distribution as possible. Lastly the variables were subjectively scrutinised to make sure the combination of variables is reasonable and to minimise the risk of random correlation. The detailed steps and results of this analysis are presented in section 5.3.

4.2.2. Network Analysis Section 3.3.2 suggested a possibility that the location of the bus terminal within the larger network of bus terminals in Stockholm can justify the different demand for layover parking in different bus terminals in the network. To validate this assumption, three local network centrality indicators were developed:

x The Degree of the bus terminal, x The Betweenness of the bus terminal, x The Closeness of the bus terminal.

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These centrality indicators identify bus terminals and bus depots as nodes in the network structure, while the vertices or edges that form the pairwise relationship can have different definitions depending on the indicator that was analysed. Implementation of different scenarios for the bus terminal network was possible using a Python script, where the Python module “Networkx” was used for creating, manipulating, and analysing each scenario in accordance with the Graph Theory (Barthélemy, 2011). All of the scenarios analysed were with undirect graphs. Where each graph represented an adjacency matrix A described by N-nodes × N-nodes, where N is the number of identified bus terminals in Stockholm county according to the definition provided in section 3.1.2 and A is defined with:

[𝐴𝑖𝑗] = {1, 𝑖𝑓 𝑖 𝑎𝑛𝑑 𝑗 𝑎𝑟𝑒 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (12)

Where

𝑖, j = bus terminal indexes

Degree Indicator As a first step in this analysis, the degree centrality indicator was provided with edges that correspond to actual bus lines that connect pairs of bus terminals, where those bus lines were terminating lines, that is, the start and the end bus terminals for bus journeys on a certain line represent the pair of nodes of the edge. Figure 4-3 is a graphical presentation for the bus terminal network, where the scenario provided suggests that contract areas had no effect on the analysis of degree centrality indicator. Here it was assumed that bus terminals that are close to each other, i.e. within walking distance, could be considered as a single bus terminal, such as Danderyds sjukhus bus terminal with Mörby station. The local degree centrality indicator for a node was the number of nodes it was connected to, and it could be computed for each bus terminal as the following and in accordance with equation (12):

𝑘𝑖 = 𝐴𝑖𝑗𝑗

(13)

Where

𝑘𝑖 = local degree indicator for bus terminal i

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Figure 4:3: Network Graph with edges representing terminating lines.

Betweenness Indicator The betweenness indicator of a certain bus terminal characterised the attractiveness of a bus terminal as an alternative location for layover activities during a deadheading between another two bus terminals. The betweenness indicator could be described as the number of shortest paths that passed the studied bus terminal as a fraction from the total shortest paths in the network, thus, it reflected the attractiveness of the location of the bus terminal for the general bus traffic flows to stop in the bus terminal without having to extend the deadheading trip duration and eventually minimising the costs associated with deadheading instruments. In accordance with equation (12), local betweenness indicator of a bus terminal could be described as the following:

𝑔𝑖 =σ𝑠𝑡(𝑖)

σ𝑠𝑡𝑠≠𝑡

(14)

Where

𝑔𝑖 = betweenness indicator for bus terminal i,

σ𝑠𝑡 = the number of shortest paths going from s to t,

σ𝑠𝑡(𝑖) = number of shortest paths going from s to t passing through bus terminal i.

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Closeness Indicator The closeness centrality of a bus terminal reflected the proximity of the bus terminal to other bus terminals in the network structure. It was logical to assume that the higher the closeness indicator for a certain bus terminal the lower the costs were that can be associated with deadheading trips towards that bus terminal, and thus, the more probable it was that this bus terminal would be chosen by an operation schedule planner as a destination for layover activities performed between the termination of one trip in one bus terminal and initiation of the next trip from another bus terminal. For this indicator, equation (12) was no longer valid as this indicator was based on the reciprocal of distances between pairs of bus terminals. Instead, edges were formed here by the shortest distance between pairs of bus terminals rather than by the existence of terminating lines. Thus, for closeness indicators, all nodes were connected by the same number of undirected edges that equals to (N-1), where N was the total number of bus terminals or depots in the network.

The distance between pairs of bus terminals was obtained from Google Maps API which offered two options of measures: travel time and travel distance. For this analysis, the travel distance was deemed to be more relevant and a better representative of a bus terminal’s centrality because travel time varied vastly depending on time of the day. The travel distance obtained by Google Maps API represents the distance of the shortest travel route of a bus from one bus terminal to another.

Three different scenarios were structured and analysed to better reflect the actual bus terminal network. Where the first scenario was a network that included all bus terminals in Stockholm region, the second scenario separated the total network of Stockholm region’s bus terminals into groups of sub-networks where every sub-network only included bus terminals and bus depots within the same contract area, i.e. the network had boundaries of the contract areas’ limits. The third scenario constituted the total Stockholm region’s network separated into groups of sub-networks where every sub-network included bus terminals and bus depots within the same operational area of a public transport operator. Operational areas had very similar boundary as the contract areas (scenario two), but this one considered adjacent contract areas of the same operator as one unified larger area since public transport operators were assumed to be able to exchange resources between those two adjacent contract areas. The following part provides further explanation:

x Scenario One: Closeness of bus terminals within Stockholm’s network

The adjacency matrix A was composed from N × N bus terminals, where N was the bus terminals in Stockholm region. A is defined with:

𝐴𝑖𝑗 = 𝑑𝑖𝑗 (15)

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Where

𝑑𝑖𝑗 = length of the shortest paths from i to j,

𝑖, 𝑗 = Bus terminals index, both i and j are bus terminals or bus depots in Stockholm region.

The local closeness centrality indicator can be obtained with the following formula:

𝐶𝑖 = 1

𝑑𝑖𝑗𝑖≠𝑗

(16)

Where

𝐶𝑖 = length of the shortest paths from i to j

x Scenario Two: Closeness of bus terminals within similar contract areas

In this scenario, it was assumed that buses serving lines in a certain contract area will only have its layover activities in facilities within the same contract area. Thus, buses cannot have its layover parking outside the contract area, and the overall bus terminal network should be separated into several smaller networks, each representing different contract area as shown in figure 4-4. Although the edges shown in figure 4-4 are presented with straight lines, they still carry a value that corresponds to the shortest path distance. The adjacency matrix was composed from 𝑁𝑘 × 𝑁𝑘 nodes of bus terminals, where k denotes an index for the contract area and 𝐴𝑘 is defined with:

𝐴𝑘,𝑖𝑗 = 𝑑𝑘,𝑖𝑗 (17)

For every contract area k, the local closeness centrality indicator was obtained with the following formula:

𝐶𝑖 = 1

𝑑𝑖𝑗𝑖≠𝑗

(18)

Where

𝑑𝑖𝑗 = length of the shortest paths from i to j,

𝑖, 𝑗 = Bus terminals index, both i and j are bus terminals or bus depots in contract area k.

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Figure 4:4: The overall terminal network separated into several networks representing each contract area

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x Scenario Three: Closeness of bus terminals within the operator’s operation areas

This scenario was possibly considering a more realistic setting since public transport operators were allowed to bid for adjacent contract areas. As section 3.1.1 indicated, a public transport operator was allowed to have its bus operations in two adjacent contract areas, there were no contractual restrictions that prohibited the operator from optimising and sharing bus fleet and other resources between those adjacent areas. This prompted a privilege for a public transport operator with adjacent contract areas to have more options of locations for layover activities with lower costs. To account for this condition, a new definition of contract area in the adjacency matrix had to be provided, where in this scenario, an operational area meant one or more contract areas with a condition that the contract areas were adjacent to each other and operated by the same public transport operator. Figure 4-5 is an example of Keolis’s operational area which constituted three adjacent contract areas; E19 HBS, E22, E19 NV. The remaining networks according to the remaining operation areas are presented in figure 4-6. Thus, the adjacency matrix was composed from 𝑁𝑝 × 𝑁𝑝 nodes of bus terminals, where p denotes an index for the operation area and 𝐴𝑝 is defined with:

𝐴𝑝,𝑖𝑗 = 𝑑𝑝,𝑖𝑗 (19)

For every operation area p, the local closeness centrality was obtained as the following:

𝐶𝑖 = 1

𝑑𝑖𝑗𝑖≠𝑗

(20)

Where

𝑑𝑖𝑗 = Length of the shortest paths from i to j,

𝑖, 𝑗 = Bus terminals index, both i and j are terminals or depots in operation area p.

Figure 4:5: Network for Keolis operation area that includes 3 different contract areas

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Figure 4:6: The overall terminal network separated into several networks representing each operation area

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5. RESULTS

In this chapter, results of the conducted analysis are presented, including results from the survey study and the processes of eliminating/choosing variables, moderation analysis and transformations. Then statistical testing of the suggested model is described along with validation executed on a different data set. The chapter then continues with discussions and elaborations on the work done. Section 5.1.3 provides a discussion on orange-rated bus terminals and reasoning for choosing them as a sample representing optimum cases of layover parking zones.

5.1. The Questionnaire This section presents the results of the survey to the public transport bus operators of Stockholm conducted using the two-part questionnaire described in section 4.1.1.

5.1.1. Driver Pause and Driver Break Facilities All public bus operators in Stockholm region found the second part of the questionnaire (that is presented in figure 4-2) to be about business-sensitive data, as it could reveal parts of their strategies in planning the unproductive part of their operations and declined to respond to that part of the survey. However, information about possible layover parking facilities outside bus terminals cannot be considered to be secret since anyone can follow a bus and identify these locations. To account for this, and since it was crucial for the analysis to identify possible locations for layover outside bus terminals, the approach for locating possible layover parking, pause and break facilities outside bus terminals was reconsidered and decided to be done by interviews.

Interview subjects included undisclosed bus drivers who had knowledge about such layover parking locations for different bus lines/contract areas. Out of respect for the right of fair competition between different public bus operators, and their desire to keep information about such locations hidden from each other, the locations of the facilities are not revealed in this thesis. This thesis only discusses the impact of these facilities on the need for layover parking in bus terminals.

Using the responses from part A of the questionnaire it was possible to identify which bus terminals provided facilities for driver break or driver pause. None of the operators deemed this information business sensitive, most likely due to most of these facilities being publicly owned. Figure 5-1 visualises the share of bus terminals that are compatible with the different layover activities. Public transport operators reported that one-third of the seventy-five bus terminals that was in use in Stockholm county did not provide access to any personnel rooms, i.e. there was no access to facilities for either driver break, nor driver pause, indicating that these bus terminals were not expected to attract demand for extended layover beyond what was motivated by other factors not associated to driver rest. Meanwhile, forty-seven percent of the bus terminals were able to accommodate both pause and break activities, since requirements from the BBA gives that all facilities that are able to cater for driver breaks also are able to cater for driver pause.

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Figure 5:1: Percentage of bus terminals in Stockholm that can facilitates pause or break activities for the drivers

5.1.2. Level of Service Assessment of layover parking in all bus terminals in accordance with the definition was successful. Figure 5-2 visualises the share of bus terminals with the different categories for the adequacy of the layover parking capacity provided, according to categories defined in section 4.1.1. The operators reported that forty-seven percent of bus terminals in Stockholm county was considered to have inadequate capacity of their layover parking zone, indicating that the operators needed to incorporate extra tactical and operational instruments in their schedule planning process in order to adapt to these capacity limitations.

Section 3.4 provided a detailed discussion on the negative impacts of these instruments on the service quality, operational costs, and environmental impact where the bus terminals rated red represent almost half of the total number of bus terminals in Stockholm. This forces the operators to use responsive measures to compensate for layover parking capacity constraints by compromising with operational cost, service resilience against abnormalities and environmental impact. It would not be a surprise to see an elevated number of deadheading trips done for the purpose of providing an adequate location for layover activities if an analysis of bus operations had been done for an AVL database (Automatic Vehicle Location).

Figure 5-2 shows that thirty-nine percent of bus terminals had an optimum layover parking capacity (orange-rated bus terminals). This means that they required minimum space allocation for the layover parking zone while at the same time it is adequate for the service intensity served at the bus terminal. Also, the thirty-nine percent value allowed to have a sample size that was large enough for the regression analysis for the majority of the variables discussed in section 4.2. Only fourteen percent of Stockholm’s bus terminals had a layover parking capacity that could accommodate an increase in the service intensity at the bus terminal, where two percent of them could accommodate larger increases in the service intensity. This should be compared to the forecasted of a population growth of twenty-five percent until 2030 and an expected increase in bus service volumes to reach the goal of an increased market share for public transport (Chapter 1).

24%

47%

29%

Share of bus terminals with pause or break facilities

Pause compatible

Break compatible

Not compatible for pause or break

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The pre-existing bus terminal level of service assessments by the Public Transport Administration of the Stockholm region (section 3.2.3) differs greatly from the results obtained in this survey study. Although all assessments used three to four tiers to describe the level of quality, this survey study was more precise about what each tier meant, and it specified that the assessment was only regarding the layover parking of the bus terminal rather than for the general level of service when all aspects were put together like the older assessments. That was the main reason for omitting the older assessments from this study. However, combining the results of all available assessments can reveal the quality level of other facilities in the bus terminal such as the capacity of bus stops and general traffic flows. Appendix C provides the detailed results from the survey of this study.

Figure 5:2: Percentages of the different gradings of Stockholm bus terminals' layover parking capacity

The opinions of the operators regarding layover parking capacity level of service were not always in consensus. When the operators did not give a bus terminal identical grading, the grading was weighted together as described in table 5-1. This resulted in twenty-eight bus terminals being rated orange, twenty-five rated red and seven rated yellow while one bus terminal was rated green. When weighting the results from non-consensual operators together no consideration to service volume in the bus terminal was taken. The quality of aggregated data is discussed in section 6.3.

Table 5-1: Resolve approach for conflicting layover grading by different operators Handling of conflicting

opinions on level of service One red One orange One yellow One green

One green N/A N/A N/A - One yellow Orange Orange - One orange Orange -

One red - Two green N/A N/A N/A - Two yellow N/A N/A - N/A Two orange Orange - Orange N/A

Two red - Red N/A N/A

47%39%

12%2%

Share of bus terminals with each rating

Red

Orange

Yellow

Green

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5.2. Eliminated Variables

In this section, the process of identifying unsuitable variables and criteria of assessment will be described. Those variables can be grouped into two categories: facility variables and centrality variables.

5.2.1. Facility Variables The on-site research along with data collected via Google Maps provided details of the layout of all bus terminals in Stockholm county. In this part of the analysis, only bus terminals representing the optimum case for layover parking were considered, i.e. orange-rated bus terminals according to the definition provided in section 4.1.1. Three main variables were obtained concerning the bus terminal planning or its layout design: number of bus stops and whether they were located inside the bus terminal or on-street stops, and the type of the bus terminal according to the bus terminal classifications presented in section 2.3.1. As provided in section 5.1.3, orange-rated bus terminals represent the optimum layover parking capacity of a bus terminal, and according to this, these facility variables were tested for possible correlation with the number of layover parking berths in orange-rated bus terminals only, for a sample size of twenty-eight bus terminals. Table 5-2 shows the correlation matrix between four different variables including the number of layover parking berths.

Table 5-2: Spearman correlation matrix for facility variables at 99% CI Terminal

stops Street stops

Total stops

Effective layover berths

Starting or terminating

lines

Terminal stops

Correlation Coefficient 1.000 -0.348 .925** .704** .795**

Sig. (2-tailed) 0.070 0.000 0.000 0.000

Street stops Correlation Coefficient -0.348 1.000 -0.031 -0.323 -0.157

Sig. (2-tailed) 0.070 0.874 0.093 0.425

Total stops Correlation Coefficient .925** -0.031 1.000 .609** .807** Sig. (2-tailed) 0.000 0.874 0.001 0.000

Effective layover berths

Correlation Coefficient .704** -0.323 .609** 1.000 .722**

Sig. (2-tailed) 0.000 0.093 0.001 0.000

Starting or terminating lines

Correlation Coefficient .795** -0.157 .807** .722** 1.000

Sig. (2-tailed) 0.000 0.425 0.000 0.000

** Correlation is significant at the 0.01 level (2-tailed)

The street stops variable refers to the bus stops that were not within the main part of the bus terminal, which usually is established for continuing bus lines, whereas the bus terminal stops refer to stops that are within the main area of the bus terminal which usually serves turning lines. The total stops variable is the summation of both types of bus stops. The correlation matrix presented in table 5-2 shows that the effective layover parking berths were correlated to the number of terminal stops, total stops, and the number of terminating or starting lines.

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The correlation matrix shows that street stops had no relation with the effective number of layover parking berths in a bus terminal, thus this variable and the total stops variable was eliminated from the analysis at this stage. Table 5-2 also shows that strong correlation existed between the number of stops in the bus terminal and the number of starting or terminating lines, and this was quite logical since both increase when turning services increase. To avoid collinearity in the regression analysis, further analysis only proceeded with one of these two variables, and only considered the number of starting or terminating lines. To examine the relation between the classification of a bus terminal’s layout and the requirements for the layover parking zone, a Point Biserial correlation test was conducted on the orange-rated bus terminals sample where dummy coding made it possible to redistribute the classification of bus terminals into three binary variables representing each category (1 = classified according to the variable’s name, 0 = classified as another category) that are presented in table 5-3. Table 5-3 shows an interesting relation: there was a negative correlation between if the bus terminal is classified as a street terminal and its needs for layover parking berths.

Table 5-3: Point Biserial correlation matrix for bus terminals classifications and layover parking requirements

Effective layover berths

Drive-through terminal

Central platform terminal

Street terminal

Effective layover berths

Point Biserial Correlation 1 -0.030 0.324 -.471* Sig. (2-tailed) 0.879 0.093 0.011

Drive-through terminal

Point Biserial Correlation -0.030 1 -.513** -.397* Sig. (2-tailed) 0.879 0.005 0.036

Central platform terminal

Point Biserial Correlation 0.324 -.513** 1 -.430* Sig. (2-tailed) 0.093 0.005 0.022

Street terminal Point Biserial Correlation -.471* -.397* -.430* 1

Sig. (2-tailed) 0.011 0.036 0.022

* Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

This particular relation could be misleading and should be addressed, although the correlation between having a street bus terminal and the number of layover parking berths was significant, the coefficient of correlation of -0.471 did not mean that street bus terminals could be used as an input for estimating the optimum layover parking capacity. According to the literature study conducted in section 2.3.1, street bus terminals are suitable when the majority of the lines to be served are continuing and non-terminating (Gunnarson & Lindqvist, 1988). It was therefore logical to have a negative correlation between street bus terminals and number of effective layover berths needed since these bus terminals are known to have relatively fewer turning lines. During planning stages of bus terminals, the decision on which type of bus terminal should be used is affected by many factors, one of those factors is the number of turning lines, thus whether the bus terminal is a street bus terminal or not should be irrelevant in this study as it was more about how many lines will turn in a specific bus terminal which was a determinant for the classification of the bus terminal. Therefore, the classification of the bus terminal design was eliminated from the regression analysis.

70

5.2.2. Centrality Variables Section 4.2.2 provided an analysis for bus terminal’s local centrality, and a discussion motivating that the higher the centrality indicator for a bus terminal, the more likely the bus terminal would be attractive for layover activities of buses coming from other bus terminals of what can be referred to as “induced demand” due to capacity limitation in other bus terminals. The three centrality indicators that were points of interest in this study were indicators of degree, betweenness and closeness. Table 5-4 shows the correlation that these indicators have with required layover parking berths for bus terminals that were assessed to have orange-rated level of operations according to the definition in section 4.1.1.

Table 5-4: Correlation matrix for different local centrality indicators Effective layover berths

Closeness Degree Betweenness Starting or terminating

lines

Effective layover berths

Correlation Coefficient 1.000 -0.283 .442* 0.333 .722**

Sig. (2-tailed) 0.145 0.018 0.084 0.000

Closeness Correlation Coefficient -0.283 1.000 -0.262 -0.076 -0.211

Sig. (2-tailed) 0.145 0.178 0.700 0.280

Degree Correlation Coefficient .442* -0.262 1.000 .872** .571**

Sig. (2-tailed) 0.018 0.178 0.000 0.001

Betweenness Correlation Coefficient 0.333 -0.076 .872** 1.000 .509**

Sig. (2-tailed) 0.084 0.700 0.000 0.006 Starting or terminating lines

Correlation Coefficient .722** -0.211 .571** .509** 1.000

Sig. (2-tailed) 0.000 0.280 0.001 0.006

* Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

The network analysis provided that the degree and the betweenness indicators considered terminating bus lines as edges for the network, whereas the closeness indicator included the shortest deadheading path as the edges of the network. The correlation matrix presented in table 5-4 shows that the local degree indicator was the only correlated indicator with the number of layover parking berths for orange-rated bus terminals. Although this may seem to be in parallel with the theory presented about a bus terminal’s connectivity within sparse network of bus terminals in section 3.3.2, yet this could be a misleading variable to be included in the regression analysis.

According to the graph theory and to the definition of edges in computing degree indicators, the centrality indicator increased with increasing number of terminating and starting lines, since this centrality indicator identified these terminating lines as edges of the network. This was also apparent in the correlation of degree indicator with the starting or terminating lines variable in table 5-4. Thus, the regression analysis proceeded with eliminating these variables, and here it was concluded that the only variable of interest to the regression analysis was the number of starting or terminating lines, whereas the others were only indicators of this variable with less relevance to the analysis.

71

5.2.3. Eliminated Bus Terminals Not all identified bus terminals could be included in the regression analysis. Some were eliminated due to no trips starting in the studied bus terminal and others due to the bus terminal being left unrated after reviewing the responses from the survey. These bus terminals are marked in appendix A.

5.3. The Model

To statistically model the optimum layover parking capacity of a bus terminal, multiple regression analysis was used. Section 3.3 provided several candidates of bus terminal and service characteristics that may have had an impact on the requirements of bus layover parking. This section describes the statistical process used to identify suitable variable candidates that were included in the final model which is presented in section 5.3.2. These processes included moderation analysis and statistical testing where any violations of regression assumptions were investigated, then the model was verified and validated in section 5.3.4 using new data samples.

5.3.1. Moderation Analysis In this section, description is given of the variable transformations and moderation analysis carried out to further analyse the interrelation between different variables that may provide better description for the requirements of layover parking. It was suspected that some variables that were provided in the case study may have had different impact on the layover parking capacity. A moderator is a factor that has an impact on the relationship between an independent variable and the dependent variable in terms of relation strength or relation direction when multiplied with the independent variable. Moreover, nonlinear transformations were also adopted to increase the relationship’s linearity, where section 5.3.3 provides an assessment for the residuals when transformations were adopted. It is worth mentioning that the process of identifying possible moderators and their appropriate transformation was interminable and was carried out simultaneously in order to check which dependent variables changed its relationship with the independent variable when a moderator changed its values (added interaction terms).

Moderation analysis in parallel with transformations was done using Python scripting in combination with Microsoft Excel. As there were a great number of different possibilities to test, and to accelerate work progress, numerous regression models were created first for early eliminations. The early regression models included different variables transformations and multiplications of different variables without prior correlation analysis using Microsoft Excel. Doing so enabled the work to early identify which transformed interaction terms had the strongest relation with the dependent variable, allowing for elimination of numerous terms from the analysis. After this early diagnosis, around thirty possible combinations of interaction terms were identified for more detailed analysis to select the most suitable interaction terms to be added to the model, which is presented in section 5.3.2. The following discussion provides more details and examples of the early regression diagnostic stage.

72

Early Regression Diagnostics After pairing all possible combinations of the independent variables with up to three variables at a time, lists of regression models were produced with all combinations of one, two, and three terms, respectively. The first forty rows in the list with three independent variables after sorting by R squared are shown in table 5-5. The length of all lists combined was in excess of 14 million rows. The list included R squared, adjusted R squared and the independent variables of the regressions with their coefficients and the constant. A few more columns were present in the original list with statistical data, which has been omitted here due to limited space. The names of the independent variables were descriptive, however in this context intended to be illustrative only. Each model was then verified for soundness and one model was selected using the method described in section 5.3.2.

Table 5-5: Early regression models to evaluate possible moderators and transformations. All variable names (ID) should be considered illustrative in this context. The table continues on the next page.

R^2 R^2-adj m1_ID m1_val m2_ID m2_val m3_ID m3_val c 0.755825 0.735477 Sq Max RT per stop ln con

meal adj RT 15 -0.00043 Ln Max RT per stop con

meal adj RT 15 0.44974 Max RT ln con coffee

adj RT 15 0.004259 0.447569

0.749136 0.72823 Sq Max RT per stop ln con meal adj RT 15

-0.00073 Max RT per stop con meal adj RT 15

0.123193 Max RT ln con coffee adj RT 15

0.005137 0.42986

0.732379 0.710078 Ln Max RT per stop ln con meal adj vol 15

-0.72526 Ln Max RT ln con coffee adj vol 15

0.644346 Max RT coffee adj RT 15

0.000603 0.161696

0.729963 0.70746 Ln Max RT per stop con RT dep 15

0.544303 Ln Max RT per stop con dep 15

-0.77882 Ln Max RT ln con coffee adj vol 15

0.955561 -2.39251

0.729731 0.707208 Ln Max RT per stop ln con meal adj vol 15

-0.82149 Ln Max RT ln con coffee adj vol 15

0.652899 Max RT per stop coffee adj product 15

0.000298 0.200953

0.726227 0.703413 Sq Max RT per stop ln con meal adj RT 15

-0.00032 Ln Max RT per stop ln con meal adj RT 15

0.203646 Max RT ln con coffee adj RT 15

0.004333 0.440182

0.72553 0.702658 Ln Max RT per stop ln con meal adj vol 15

-0.71919 Ln Max RT ln con coffee adj vol 15

0.727931 Max RT RT dep 15 0.000708 0.082607

0.725347 0.702459 Sq Max RT per stop ln con meal adj RT 15

-0.00028 Ln Max RT con meal adj RT 15

0.173441 Max RT ln con coffee adj RT 15

0.004076 0.48357

0.724121 0.701131 Sq Max RT per stop ln con meal adj vol 15

-0.33816 Ln Max RT med freq coffee adj product 15

-0.11274 Ln Max RT ln con coffee adj vol 15

1.074813 0.165184

0.723945 0.70094 Ln Max RT per stop ln con meal adj vol 15

-0.79539 Ln Max RT ln con coffee adj vol 15

0.71151 Max RT per stop product dep 15

0.000388 0.138374

0.723932 0.700926 Ln Max RT per stop ln con meal adj RT 15

0.393015 Max RT per stop ln con meal adj RT 15

-0.03288 Max RT ln con coffee adj RT 15

0.004563 0.408235

0.723098 0.700023 Sq Max RT per stop ln con meal adj vol 15

-0.33771 Ln Max RT med freq product dep 15

-0.12053 Ln Max RT ln con coffee adj vol 15

1.075184 0.16372

0.721249 0.69802 Sq Max RT per stop ln con meal adj vol 15

-0.36358 Ln Max RT ln con meal adj product 15

0.07041 Ln Max RT ln con coffee adj vol 15

0.778652 0.211988

0.720405 0.697105 Exp Max RT per stop ln con meal adj vol 15

-0.24446 Ln Max RT ln con meal adj product 15

0.079152 Ln Max RT ln con coffee adj vol 15

0.769442 0.467899

0.720231 0.696917 Sq Max RT coffee adj RT 15

3.11E-07 Ln Max RT per stop ln con meal adj vol 15

-0.767 Ln Max RT ln con coffee adj vol 15

0.750046 0.165073

0.716245 0.692599 Ln Max RT con meal adj RT 15

0.293766 Max RT per stop ln con meal adj RT 15

-0.02595 Max RT ln con coffee adj RT 15

0.004071 0.494248

0.716177 0.692526 Ln Max RT ln con coffee adj vol 15

0.924158 Ln Max RT ln con product dep 15

-0.58484 Ln Max RT RT dep 15 1.075016 -1.90048

0.716177 0.692526 Ln Max RT ln con coffee adj vol 15

0.924158 Ln Max RT ln con dep 15 -0.58484 Ln Max RT RT dep 15 0.490173 -1.90048

0.715822 0.692141 Ln Max RT per stop ln con dep 15

-0.53823 Ln Max RT per stop RT dep 15

0.45931 Ln Max RT ln con coffee adj vol 15

0.884295 -1.86566

0.715779 0.692094 Exp Max RT per stop ln con meal adj vol 15

-0.21279 Ln Max RT med freq coffee adj product 15

-0.1162 Ln Max RT ln con coffee adj vol 15

1.081504 0.38983

0.714878 0.691117 Sq Max RT per stop ln con meal adj RT 15

-0.00026 Ln Max RT ln con meal adj RT 15

0.114172 Max RT ln con coffee adj RT 15

0.004199 0.473309

0.714764 0.690994 Sq Max RT per stop ln con meal adj vol 15

-0.34132 Ln Max RT ln con coffee adj vol 15

0.784991 Ln Max RT meal adj product 15

0.058774 0.217656

0.713662 0.6898 Ln Max RT per stop con dep 15

-0.4909 Ln Max RT ln con coffee adj vol 15

0.772751 Ln Max RT con RT dep 15

0.365359 -1.89406

0.712967 0.689048 Exp Max RT per stop ln con meal adj vol 15

-0.22694 Ln Max RT ln con coffee adj vol 15

0.776354 Ln Max RT meal adj product 15

0.065675 0.455627

0.712678 0.688735 Exp Max RT per stop ln con meal adj RT 15

-1.7E-33 Ln Max RT per stop con coffee adj product 15

0.357337 Sq bus stops 0.004937 -0.02411

0.712254 0.688275 Exp Max RT per stop con meal adj RT 15

-5.2E-12 Ln Max RT per stop con coffee adj product 15

0.357679 Sq bus stops 0.004919 -0.02382

0.709129 0.68489 Ln Max RT per stop ln con meal adj vol 15

-0.8014 Ln Max RT per stop con coffee adj product 15

0.264029 Max RT coffee adj vol 15

0.036113 -0.01136

0.708739 0.684467 Exp Max RT per stop con meal adj vol 15

-1.6296 Ln Max RT ln con coffee adj vol 15

0.711841 Ln Max RT con meal adj RT 15

0.172611 1.888289

0.707989 0.683655 Ln Max RT per stop meal adj product 15

-1.55767 Ln Max RT meal adj product 15

1.244814 Max RT per stop coffee adj RT 15

0.013075 0.342464

0.70786 0.683515 Ln Max RT per stop ln con dep 15

-0.53278 Ln Max RT ln con coffee adj vol 15

0.766162 Ln Max RT ln con RT dep 15

0.375374 -1.76709

0.707705 0.683347 Sq Max RT per stop ln con meal adj vol 15

-0.37844 Ln Max RT ln con meal adj vol 15

0.288552 Ln Max RT ln con coffee adj vol 15

0.747597 0.284911

0.707217 0.682819 Exp Max RT per stop ln con meal adj vol 15

-0.20826 Ln Max RT med freq dep 15

-0.39713 Ln Max RT ln con coffee adj vol 15

1.0772 0.379827

0.706782 0.682347 Ln Max RT per stop ln con meal adj vol 15

-0.70744 Ln Max RT per stop con coffee adj product 15

0.284949 Max RT coffee adj RT 15

0.000815 -0.00936

0.706567 0.682115 Ln Max RT per stop con dep 15

-0.3105 Ln Max RT ln con coffee adj vol 15

0.737034 Ln Max RT ln con RT dep 15

0.346236 -1.92591

0.705763 0.681243 Ln Max RT per stop ln con RT dep 15

0.672796 Max RT per stop ln con dep 15

-1.74044 Max RT ln con coffee adj vol 15

0.180399 -0.45731

0.705661 0.681132 Sq Max RT per stop ln con meal adj RT 15

-0.00026 Ln Max RT per stop con meal adj product 15

0.149 Max RT ln con coffee adj RT 15

0.004061 0.512016

0.705294 0.680735 Ln Max RT per stop ln con RT dep 15

0.405072 Ln Max RT per stop con dep 15

-0.44503 Ln Max RT ln con coffee adj vol 15

0.900462 -2.01901

0.705257 0.680695 Ln Max RT per stop ln con meal adj vol 15

-0.75669 Ln Max RT ln con coffee adj vol 15

0.734996 Max RT ln con coffee adj product 15

0.0000445 0.187268

0.705096 0.680521 Exp Max RT per stop ln con meal adj RT 15

-1.6E-33 Ln Max RT per stop ln con coffee adj RT 15

0.437182 Bus stops 0.138154 -0.78843

73

5.3.2. Variables Selection Section 5.3.1 provided that early prediction of possible moderators can be possible through running early regressions for different variable combinations simultaneously with their transformations. The early regression diagnostics suggested thirty different combinations of variables and interaction terms with good adjusted R2 value. This section describes the process used to ensure a model with the most appropriate interaction terms and variables to be included and provides further analysis for the different interaction terms between different models from the previous stage.

Detailed Regression Diagnostics The thirty different regression models that passed from the early regression diagnostics were further assessed for the following criteria:

x Adjusted R2 value and its change from the R2 value. x Number of terms included in the model. x Correlation between dependent and independent variables. x Multicollinearity diagnostics and model significance. x Residuals diagnostics.

The statistical tests for the selected term combinations are presented in section 5.3.3. Before identifying the model that includes the most suitable combination of variables or interaction terms, a more detailed moderation analysis was done. The detailed moderation analysis included testing the hypothesis that some independent variables had stronger relations with the dependent variable when moderators were interacting with them. The early diagnostics helped in identifying suitable variables with possible interactions among them that may have had a good potential in describing the optimum number of layover parking berths of bus terminals (Nberths-optimum), these variables in their raw state (no interactions or transformations included) are described below whereas their correlation matrix is presented in table 5-6:

Cn = Maximum number of timed connections at the bus terminal (connections per hour)

TR = The sum of departing trip runtime during the fifteen minutes with the highest sum of departing trip runtime during the day (minutes)

V15 = The number of departing trips during the fifteen minutes with the highest sum of departing trip runtime during the day

Fc = Pause factor, calculated according to section 4.2.1.

Table 5-6: Spearman's correlation matrix for raw variables Nberths-optimum Cn TR Fc V15 Nberths-optimum Correlation Coefficient 1.000 -0.002 .663** .381* .597** Cn Correlation Coefficient -0.002 1.000 0.219 -0.011 0.355 TR Correlation Coefficient .663** 0.219 1.000 0.017 .868** Fc Correlation Coefficient .381* -0.011 0.017 1.000 0.123 V15 Correlation Coefficient .597** 0.355 .868** 0.123 1.000 * Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

74

The correlation matrix presented in table 5-6 uses “Spearman’s ρ” to describe a monotonic relationship, since this relation was not yet verified to be linear. Here it was concluded that both Tr and V15 variables were strongly correlated with the optimum number of layover parking berths in bus terminals within a 99% confidence interval, whereas Cn and Fc were not correlated with any of the independent variables and with a poor correlation with dependent variable of the optimum number of layover parking berths for Fc.

The hypothesis was that both Cn and Fc can form a moderator influence on V15 and Tr, where if their interactions were included in the model, the model would have had better predictions for the dependent variable. To test this hypothesis, interaction terms were included in a Pearson’s correlation matrix, assuming that the relationship between each variable or interaction term was linear with the dependent variable, as table 5-7 presents, where the three included terms were:

𝑥1 = 𝑇𝑟 (no interaction term)

𝑥2 = 𝑉15 × 𝑓𝑐 𝑐𝑛⁄ (added interaction term)

𝑥3 = 𝑉15 𝑐𝑛⁄ (added interaction term)

Table 5-7: Pearson's correlation matrix for interaction terms added Nberths-optimum X1 = Tr X2 X3 Nberths-optimum Pearson Correlation 1 .652** .591** .547** X1 Pearson Correlation .652** 1 0.304 0.351 X2 Pearson Correlation .591** 0.304 1 .932** X3 Pearson Correlation .547** 0.351 .932** 1 ** Correlation is significant at the 0.01 level (2-tailed)

The correlation matrix in table 5-7 shows that both interaction terms X2 and X3 and variable Tr had a strong correlation with the optimum number of layover parking berths in a bus terminal within a confidence interval of 99%. By adding the 𝑓𝑐 𝑐𝑛⁄ and the 1 𝑐𝑛⁄ factors to 𝑉15 terms the correlation was strengthened as the new correlation coefficients indicate when compared with the correlation coefficients from table 5-6. As previously mentioned, a linear relationship was assumed in this correlation matrix. To verify this assumption, a scatter plot for each interaction term can reveal how linear the relationship is and whether transformations were needed to ensure linearity and prompt further enhancement in Pearson’s correlation coefficients. The figures 5-3, 5-4, and 5-5 show scatter plots for each variable/interaction term with a best fitted line.

75

Figure 5:3: Scatter plot for X1 vs optimum number of layover parking berths

Figure 5:4: Scatter plot for X2 vs optimum number of layover parking berths

Figure 5:5: Scatter plot for X3 vs optimum number of layover parking berths

Transformations The scatter plots in figures 5-3, 5-4 and 5-5 show more clustering of data points in one side of the plot than the other, especially in figures 5-4 and 5-5. This kind of grouping or clustering lowers the apparent linearity of the relationships presented in these graphs. To account for this clustering, logarithmic transformation was introduced to create a better linear spread of the data. Figures 5-6, 5-7 and 5-8 represent the same variables or interaction terms but with incorporating natural logarithmic transformations. The three transformed terms were:

𝑥1′ = 𝐿𝑛(𝑇𝑟) (no interaction term)

𝑥2′ = 𝐿𝑛(𝑉15) × 𝐿𝑛(𝑓𝑐) 𝐿𝑛(𝑐𝑛)⁄ (added interaction term)

𝑥3′ = 𝐿𝑛(𝑉15) 𝐿𝑛(𝑐𝑛)⁄ (added interaction term)

02468

101214

0 200 400 600 800 1000 1200

Nbe

rths

-opt

imum

푥1= 𝑇푟 (minutes)

푥1

0

5

10

15

0 1 2 3 4 5 6 7 8

Nbe

rths

-opt

imum

푥2= 𝑉15 × 𝑓𝑐 ⁄ 𝑐푛

푥2

0

5

10

15

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5Nbe

rths

-opt

imum

푥3= 𝑉15 ⁄ 𝑐푛

푥3

76

Figure 5:6: Scatter plot for transformed X1 vs optimum number of layover parking berths

Figure 5:7: Scatter plot for transformed X1 vs optimum number of layover parking berths

Figure 5:8: Scatter plot for transformed X1 vs optimum number of layover parking berths

The scatter plots in figures 5-6, 5-7 and 5-8 show improvements in the linearity between different interaction terms with the dependent variable, where the point data have a clearer directional spread. To confirm if a stronger and more linear relation was achieved with natural logarithmic transformations, Pearson’s correlation matrix for the transformed interaction terms/variables is presented in table 5-8. By comparing the correlation coefficients in table 5-8 with table 5-7, all of the transformed interaction terms or variables were showing improvements in Pearson’s coefficients, indicating stronger linear relationships between these terms and the dependent variable.

Table 5-8: Pearson's correlation matrix for the transformed interaction terms. Ln (Nberths-optimum) X1’ X2’ X3’ Ln (Nberths-optimum) Pearson Correlation 1 .679** .773** .608** X1′ Pearson Correlation .679** 1 .617** .731** X2′ Pearson Correlation .773** .617** 1 .838** X3′ Pearson Correlation .608** .731** .838** 1 ** Correlation is significant at the 0.01 level (2-tailed) 𝑥n′ denotes 𝑥n after transformation with natural logarithm

0

1

2

3

0 1 2 3 4 5 6 7 8

Ln(N

bert

hs-o

ptim

um)

푥1= Ln(𝑇푟)

Transformed 푥1

00.5

11.5

22.5

3

0 0.5 1 1.5 2 2.5 3 3.5

Ln(N

bert

hs-o

ptim

um)

푥2= Ln(𝑉15)× Ln(𝑓𝑐) ⁄ Ln(𝑐푛)

Transformed 푥2

00.5

11.5

22.5

3

-1 -0.5 0 0.5 1 1.5 2 2.5 3

Ln(N

bert

hs-o

ptim

um)

푥3= Ln(𝑉15) ⁄ Ln(𝑐푛)

Transformed 푥3

77

The Selected Model The previous analysis led to the conclusion that the best model to describe the requirement for the amount of layover parking was by combining the following variables Xn, sorted by their average impact on the estimated layover parking requirement in the twenty-eight orange-rated bus terminals:

𝑥1 : The natural logarithm of the sum of departing trip runtime during the fifteen minutes with the highest sum of departing trip runtime during the day.

𝑥2 : The natural logarithm of all the following: the number of departing trips during the fifteen minutes with the highest sum of departing trip runtime during the day multiplied by the pause factor from section 4.2.1 and divided by the natural logarithm of the maximal number of pulses per hour due to timed connections. If there was no timed connection in the studied bus terminal 60 pulses per hour were assumed, i.e. one pulse per minute.

𝑥3 : The natural logarithm of all the following: the number of departing trips during the fifteen minutes with the highest sum of departing trip runtime during the day divided by the natural logarithm of the maximal number of pulses per hour due to timed connections, using the same definition used in x2

Using the stepwise insertion method of the IBM SPSS Statistics software, the regression model was decided to be (coefficients rounded to three decimals):

ln (𝑁𝑜𝑝𝑡𝑖𝑚𝑢𝑚 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦) = 0.490𝑥1 + 0.924𝑥2 − 0.585𝑥3 − 1.90 (21)

Where

𝑥1′ = 𝐿𝑛(𝑇𝑟)

𝑥2′ = 𝐿𝑛(𝑉15) × 𝐿𝑛(𝑓𝑐) 𝐿𝑛(𝑐𝑛)⁄

𝑥3′ = 𝐿𝑛(𝑉15) 𝐿𝑛(𝑐𝑛)⁄

By substituting the actual variables and interaction terms included in 𝑥1, 𝑥2 and 𝑥3, equation (21) was rewritten:

𝐿𝑛(𝑁𝑜𝑝𝑡𝑖𝑚𝑢𝑚 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦) = −1.90 + 0.490 𝐿𝑛(𝑇𝑟) + 0.924 𝐿𝑛(𝑉15) × 𝐿𝑛(𝑓𝑐)

𝐿𝑛(𝑐𝑛)− 0.585

𝐿𝑛(𝑉15)𝐿𝑛(𝑐𝑛)

(22)

Or

𝐿𝑛(𝑁𝑜𝑝𝑡𝑖𝑚𝑢𝑚 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦) = −1.90 + 0.490 𝐿𝑛(𝑇𝑟) + 𝐿𝑛(𝑉15)𝐿𝑛(𝑐𝑛)

( 0.924 𝐿𝑛(𝑓𝑐) − 0.585) (23)

78

To eliminate the natural logarithmic transformation of the dependent variable, exponentiation rules were applied along with Euler's number distribution on both sides of the equation. This produced the following series of simplifications:

𝑒𝐿𝑛(𝑁𝑜𝑝𝑡𝑖𝑚𝑢𝑚 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦) = 𝑒−1.9 × 𝑒0.490 𝐿𝑛(𝑇𝑟) ×𝑒0.924

𝐿𝑛(𝑉15) × 𝐿𝑛(𝑓𝑐)𝐿𝑛(𝑐𝑛)

𝑒0.585

𝐿𝑛(𝑉15)𝐿𝑛(𝑐𝑛)

(24)

𝑁𝑜𝑝𝑡𝑖𝑚𝑢𝑚 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 = 0.15𝑇𝑟0.490 ×

(𝑒𝐿𝑛(𝑉15)𝐿𝑛(𝑐𝑛) )

0.924 𝐿𝑛(𝑓𝑐)

(𝑒𝐿𝑛(𝑉15)𝐿𝑛(𝑐𝑛) )

0.585 (25)

The suggested model that predicts the optimum layover parking capacity needed for a bus terminal in Stockholm county was:

𝑵𝒐𝒑𝒕𝒊𝒎𝒖𝒎 𝒄𝒂𝒑𝒂𝒄𝒊𝒕𝒚 = 𝟎. 𝟏𝟓𝑻𝒓𝟎.𝟒𝟗𝟎 𝒆

𝑳𝒏(𝑽𝟏𝟓)𝑳𝒏(𝒄𝒏)

𝟎.𝟗𝟐𝟒 𝑳𝒏(𝑭𝒄)−𝟎.𝟓𝟖𝟓

(26)

Where

Cn = Maximum number of timed connections at a bus terminal (connections per hour), if no timed connection is present it was set to 60

TR = The sum of departing trip runtime during the fifteen minutes with the highest sum of departing trip runtime during the day (minutes)

V15 = The number of departing trips during the fifteen minutes with the highest sum of departing trip runtime during the day

Fc = Pause factor, calculated according to section 4.2.1.

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The data set of orange-rated bus terminals were predicted by this model to have the layover parking requirement listed in table 5-9.

Table 5-9: Evaluation and regression results for orange-rated bus terminals

Bus terminals rated orange

Actual layover parking capacity x1 x2 x3

Modelled layover parking capacity

requirement Residual Standardised

residual

Alvik T-bana 3 5.00 0.86 0.57 2.75 0.25 0.25 Brandbergen centrum 1 5.72 0.00 1.50 1.03 -0.03 -0.17 Brommaplan T-bana 11.5 6.56 2.21 1.68 10.78 0.72 0.47 Farsta T-bana 2 5.64 1.29 1.17 3.93 -1.93 -1.65 Högdalen T-bana 2 5.37 1.29 1.04 3.72 -1.72 -1.50 Huddinge station 8 5.21 1.57 0.88 4.92 3.08 2.44 Jakobsberg station 11 6.44 2.21 2.10 7.94 3.06 2.12 Jordbro station 3 4.55 1.75 1.06 3.79 -0.79 -0.68 Kallhäll station 6 4.81 1.06 0.88 2.52 3.48 3.62 Karolinska sjukhuset norra 10 6.47 1.48 1.08 7.48 2.52 1.78

Kista T-bana 5 6.30 1.87 1.47 7.85 -2.85 -1.99 Kungens Kurva 2 5.19 0.00 -0.02 1.93 0.07 0.09 Märsta station 12 6.02 3.01 2.51 10.62 1.38 0.90 Norrtälje busstation 7 6.92 2.18 1.86 11.23 -4.23 -2.72 Nynäshamn station 5 5.24 1.06 1.06 2.79 2.21 2.18 Odenplan T-bana 2 5.33 0.86 0.57 3.23 -1.23 -1.13 Rimbo busstation 6.65 5.46 1.03 0.80 3.51 3.14 2.80 Ropsten T-bana 6 5.96 2.59 1.92 9.83 -3.83 -2.53 Södertälje hamn station 1 3.78 0.11 -0.58 1.49 -0.49 -0.77

Sollentuna station 11.25 6.04 2.04 1.75 6.84 4.41 3.18 Täby centrum station 5.25 5.30 1.72 1.21 4.85 0.40 0.32 Telefonplan T-bana 2 4.30 1.04 0.70 2.13 -0.13 -0.15 Tumba station 6 5.89 2.10 1.67 7.04 -1.04 -0.75 Universitetet T-bana 5 4.99 0.88 0.19 3.49 1.51 1.35 Vallentuna station 3 5.40 1.03 1.03 2.99 0.01 0.01 Årstaberg station 1 4.03 0.37 0.37 1.22 -0.22 -0.49 Älmsta busstation 4 4.32 1.47 1.06 2.59 1.41 1.45 Ösmo centrum 1 4.38 1.06 1.06 1.83 -0.83 -1.07

5.3.3. Statistical Testing The proposed model for describing the requirement of layover parking capacity in a generalised bus terminal with operational requirements similar to Stockholm’s bus terminals should have a low risk of random correlation but have a reasonable chance of giving a correct value on average. The model should also have a normal distributed set of residuals, a relatively low correlation between its independent variables and reasonable P-values for each independent variable and an ability to correctly predict a higher layover parking capacity requirement than the actual layover parking capacity in red-rated bus terminals, assertation of this is provided in section 5.3.4.

The four tables below provide some statistical data on the regression analysis of the selected model for layover parking requirement in bus terminals. Table 5-10 shows that the selected model’s prediction of the natural logarithm of layover capacity requirement correlates to almost eighty-five percent with the natural logarithm of the actual recorded layover capacity in the twenty-eight bus terminals used to fit the model, with an adjusted R squared of sixty-nine percent. No seemingly sound models

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with fewer independent variables had an adjusted R squared that high, the highest with two independent variables was sixty-six percent and all model candidates with two independent variables with adjusted R squared above sixty percent failed in reasonably predicting which bus terminals were described as being congested by the operators.

Table 5-10: Model summary Regression Statistics Multiple R 0.84627268 R Square 0.71617745 Adjusted R Squared 0.69252557 Standard Error 0.45278087 Observations 28

Table 5-11 below presents the analysis of variation (ANOVA) for the selected model. The significance F was much lower than the threshold (0.05) for accepting the model in this stage.

Table 5-11: ANOVA results ANOVA df SS MS F Significance F

Regression 3 12.415412 4.13847068 20.186626 9.4581E-07 Residual 24 4.92025247 0.20501052

Total 27 17.3356645

Table 5-12 below gives more information on the selected model. All independent variables were significant on the 95 percent significance level, with the worst scoring at a risk of being insignificant at 4.4 percent, still below the threshold at five percent. All variance inflation factors (VIF) were below 5 indicating that multicollinearity risk was minimal.

Table 5-12: Regression coefficients

Coefficients Standard Error t Stat P-value Lower

95% Upper 95% VIF

Intercept -1.900 0.720 -2.641 0.014 -3.389 -0.412 x1 0.490 0.158 3.101 0.005 0.163 0.817 2.147 x2 0.924 0.210 4.399 0.000 0.490 1.359 3.350 x3 -0.585 0.276 -2.122 0.044 -1.155 -0.015 4.452

To further assess the risk of collinearity, table 5-13 is a complete collinearity diagnostic output through singular value decomposition. All of the dimensions for different variables were lower than 30 for the condition index, and the variance proportions were not above 0.9 for a pair of variables for the same dimension, indicating that each variable had a unique and valuable contribution to the variation in the dependent variable.

Table 5-13: Collinearity diagnostics

Dimension Eigenvalue Condition Index Variance Proportions

(Constant) X1 X2 X3 1 3.749 1.000 0.00 0.00 0.00 0.00 2 0.205 4.278 0.02 0.01 0.07 0.08 3 0.041 9.579 0.00 0.00 0.92 0.63 4 0.006 25.488 0.97 0.99 0.00 0.28

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To assess the capability of the model for predictions on different ranges of the dependent variable, a scatter plot for the residuals can reveal the risk for heteroscedasticity. Figure 5-9 is a scatter plot for the residuals on different values for the predict variable, it also shows that the variance for the predicted value was constant throughout the plot with no apparent dependencies between residuals. Figure 5-10 is a standardised residuals plot where all values were within both x-value range and y-value range of -2 and 2, suggesting an absence of residuals outliers.

Figure 5:9: Model's residuals plot

Figure 5:10: Model's standardised residuals plot

In order to be able to make valid inferences from a regression model, it is necessary that the residuals have a normal distribution. Figure 5-11 is a probability-probability plot, where data points appear to follow the central line with minor deviations. To further assess the normality of the residuals, Shapiro-Wilk normality test results are presented in table 5-14, where it shows that the results were insignificant, and failed to reject the hypothesis that the sample followed a normal distribution.

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Figure 5:11: Normal P-P plot for the standardised residuals

Table 5-14: Shapiro-Wilk normality test results

Statistic df Sig. Unstandardised Residual (Shapiro-Wilk) 0.964 28 0.438

5.3.4. Model Validation

Figure 5:12: Actual vs predicted layover berths for all bus terminals in Stockholm

When performing regression analysis, it is vital to check the model towards reference data not included in the creation of the model. Due to the small number of bus terminals found operating on their layover parking capacity limit (twenty-eight bus terminals in the data set) the model was validated using data from bus terminals

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described by the public bus operators of Stockholm as congested (rated red) and from bus terminals described by the public bus operators of Stockholm as operating slightly below their layover parking capacity limit (rated yellow), giving an additional thirty-two bus terminals, twenty-five of which were described as congested (rated red). Figure 5-12 shows their actual layover parking capacity versus their modelled layover parking capacity requirement, with a grey line showing the 1:1 ratio. The colours in Figure 5-12 match with the colour codes used in the questionnaire. If the model were optimal (R squared exactly equal to one) and the input data were perfect, all yellow dots would be below the grey line, all orange dots on the grey line and all red dots above the grey line. The trend line is based on the least-squares method. Only one bus terminal included in the study (Åkersberga station) was marked by the operators as having more than sufficient layover parking capacity (rated green) and it had capacity for eighteen buses and was modelled to require a capacity of 6.39 buses. It was not included in the tables or graphs in this section since it was deemed to be an outlier in several ways, one being that it had been constructed as a bus depot.

The selected model for bus terminal layover parking capacity predicted a higher layover parking requirement than the actual capacity for all but four of the bus terminals described as congested (rated red) by the operators. The bus terminals described as operating slightly below their layover parking capacity limit (rated yellow) could not be distinctly identified by the selected model. Tables 5-15 and 5-16 show the modelled layover parking capacity requirements for the bus terminals described as being congested (rated red) by the operators.

Table 5-15: Evaluation and regression results for red-rated bus terminals (without outliers)

Bus terminals rated red Layover parking

capacity (real world)

Estimated requirement for layover parking

capacity Residual Standardised

residual

Akalla T-bana 5 1.39 3.61 6.28 Flemingsberg station 0 0.60 -0.60 -0.83 Älvsjö station 1 3.56 -2.56 -2.27 Farsta Strand station 0 2.30 -2.30 -2.52 Fittja T-bana 0 1.55 -1.55 -2.34 Fruängen T-bana 3.5 4.05 -0.55 -0.47 Gullmarsplan T-bana 15 17.75 -2.75 -1.62 Hallonbergen T-bana 1 1.28 -0.28 -0.56 Huddinge sjukhus 1 2.89 -1.89 -1.84 Kungsängen station 2 4.27 -2.27 -1.89 Liljeholmen T-bana 5 9.79 -4.79 -3.17 Skärholmen T-bana 2.1 4.24 -2.14 -1.78 Slussen T-bana 12 15.10 -3.10 -1.88 Södertälje centrum station 3 8.27 -5.27 -3.63 Köpmangatan 0 0.89 -0.89 -2.66 Solna centrum T-bana 2.7 4.13 -1.43 -1.20 Spånga station 5 7.34 -2.34 -1.66 Sundbyberg station 3 3.30 -0.30 -0.27 Trollbäcken centrum 0 1.11 -1.11 -3.44 Tullinge station 1 4.13 -3.13 -2.63 Tyresö centrum 2 4.20 -2.20 -1.84 Vällingby T-bana 4 6.65 -2.65 -1.93

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Three of the region’s largest bus terminals were described as congested (rated red) by the operators while the selected model did not predict this accurately. Very few of the reviewed models accurately described them as congested and they were to be considered outliers, this is further discussed in section 6.1.

Table 5-16: Evaluation and regression results for red-rated outlier bus terminals

Outlier bus terminals rated red

Layover parking capacity (real

world)

Estimated requirement for layover parking

capacity Residual Standardised

residual

Handen station 16 6.07 9.93 7.39 Danderyds sjukhus T-bana 23.5 14.10 9.40 5.78 Tekniska Högskolan T-bana 16 5.60 10.40 7.93

Seven bus terminals were described by the operators as having more layover parking capacity than currently required (rated yellow). The selected model could not correctly predict them as a group which is discussed in section 6.1. Results from the selected model are shown in table 5-17.

Table 5-17: Evaluation and regression results for yellow-rated terminals

Bus terminals rated yellow

Layover parking capacity (real world)

Estimated requirement for layover parking

capacity Residual Standardised

residual

Hallunda T-bana 4.25 2.62 1.63 1.66 Järna station 1 2.81 -1.81 -1.78 Norsborg T-bana 1 4.74 -3.74 -3.00 Ösmo station 1 0.15 0.85 0.62 Östertälje station 4 4.25 -0.25 -0.21 Upplands Väsby station 9.5 10.13 -0.63 -0.41 Västerhaninge station 3 4.15 -1.15 -0.96

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6. DISCUSSION

The regression model includes three interaction terms that were previously suspected to have direct impact on layover parking capacity in bus terminals. The first variable (x1) is the summation of departing trips runtime during the fifteen minutes with the highest sum of departing trip runtime during the day (minutes). This variable covers two aspects of bus operations. When demand for layover parking was analysed in section 3.3, it was apparent that not only the number of terminating or starting lines would increase the demand for layover parking, but also the headways of these terminating lines, where this is the first aspect that variable (x1) covers.

The second aspect that (x1) covers is the effect of legal agreements. Although (x1) does not provide explicit expressions about the probability that a certain trip will need to have a layover due to compliance with unions regulations, yet it incorporates this effect through the run time of each trip. The absence of incorporating probability distribution for a trip to have a layover due to the relation between the runtime and unions’ regulations in the model adds further robustness to the final model as it becomes a nonparametric model.

The second and the third interaction terms (x2 and x3) are very similar, both include the number of departing trips for the same time period and both include the connections variable. This was a reason for including more detailed multicollinearity diagnostics in the study, where it was apparent that the pause factor included in (x2) made them distinct from each other and that they have a unique explanation for the variance of the dependent variable. In general, regression results which have interaction terms are interpreted differently than those that have standalone variables in its terms, the significance of the term reflects the significance of the interaction term included and not the variables that constitute it. This can explain why the natural logarithmic transformations were found to have a better linear relationship with the dependent variable when they were introduced directly to the variables included in the interaction terms and not to the interaction term as a whole.

Similar to the rationale discussed in the case study on what creates a demand for layover parking in a bus terminal, the regression model recognises all of the service variables provided in section 3.3.1 to have an influence on the required capacity for the layover parking zone. Three of the service variables presented in section 3.3.1, namely: the effects of turning lines, headways, and timed connections, can be generalised for any network of bus terminals outside Stockholm county nationally or internationally, since these variables have the same influence over layover capacity requirement no matter what the local regulations are. On the other hand, the effect of union’s agreements requires new analysis when the model is considered for a bus terminal network outside Stockholm county or if local regulations change, since the probability to have a break or pause activity will change for the same runtime. Which means that 𝑉15and 𝐶𝑛 in the final model in equation (26) can be used without future calibration while 𝑇𝑟 and 𝑓𝑐 will need calibration in different networks of bus terminals.

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The effect of a personnel room that is discussed in section 3.3.2 was also partly incorporated in the regression model using the pause factor. This effect can be seen as a direct demand-supply interaction, where the absence of an infrastructure for the needed demand can extensively reduce layover activities in a certain bus terminal, which is discussed further in section 6.1. According to the case study on Stockholm county, the majority of the demand for layover parking is a product of scheduling techniques practiced by the public transport operators and the design criteria that are incorporated during the scheduling process. This part of the demand has been captured by the regression model with R2 adjusted value of 0.69. It can be assumed that a share of the demand is induced from insufficient supply or capacity limitations in other bus terminals, of which the study was unable to detect with the considered small study sample, as it may have accounted for some of the remaining 0.31 of R2 value that the regression model did not manage to account for. In general, the proposed model does a reasonably good job at predicting the bus terminals it was fitted against, the variables make reasonable sense and it recognises the absolute of the majority of the red-rated bus terminals as congested.

6.1. Comments on Bus Terminals

Unexpectedly the selected model did not fit all bus terminals. The model fits most bus terminals quite well, but some residuals need to be commented and be reflected upon. Since bus operations is a very complex matter it is virtually impossible to implement all aspects of what decides the requirement for layover parking capacity in a bus terminal and the selected model does a better job at predicting some bus terminals than other bus terminals. In table 6-1 some of the less correct predictions are discussed. All bus terminals used for regression analysis were marked by the operators as operating just slightly below their layover parking capacity limit (orange-rated).

Table 6-1: Comments on nonvalidating red-rated bus terminals, the table spans three pages.

Name Actual capacity Modelled capacity requirement Difference Akalla T-bana 5 1.39 3.61 Used for validation only. Red-rated by one operator, with a comment stating that it would be good to add a driver pause possibility. There is a pause enabling facility in the metro that has been used by bus operators before, why it cannot be used by this particular operator is outside the scope of this study. Given the comment by the operator there are reasons to believe that the categorisation of this bus terminal is wrong and if one operator claims there is no pause facility also the availability of facilities could be reviewed. No other operator rated this bus terminal. Name Actual capacity Modelled capacity requirement Difference Danderyds sjukhus T-bana 23.5 14.10 9.40 Used for validation only. This is one of the largest bus terminals in the Stockholm metropolitan area, serving the north eastern sector. The bus terminal is the one with the highest recorded layover parking capacity in this study (the second largest got a capacity of sixteen buses) and it can be assumed to be an outlier. One possible reason could be that it is used for more driver breaks than in an average bus terminal and another possible reason is that it is used as a daytime bus parking for some vehicles belonging to the Norrtälje bus depot, located some 60 kilometres away, relieving the Tekniska högskolan bus terminal. Another possible reason is that the share of the induced demand from the total layover demand in this bus terminal is larger than other common bus terminals, which the regression model is unable to account for.

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Name Actual capacity Modelled capacity requirement Difference Handen station 16 6.07 9.93 Used for validation only. The temporary bus terminal at Handen station is located along a major road with a long kerbside layover parking and the one operator rating it red did not elaborate on why. Using the assumed capacity (sixteen buses) could possibly be in violation with union agreements due to long walking distances or other safety concerns. It is also possible that there are more driver breaks in this bus terminal than in the average bus terminal. Name Actual capacity Modelled capacity requirement Difference Huddinge station 8 4.92 3.08 Used for regression analysis. The difference could be exaggerated here, since there are only three dedicated berths and space along the sides of the bus terminal, meaning that the assumed usable capacity of eight buses could be higher than what the practical limit is. There is also a possibility that this bus terminal is used for more driver breaks than the average bus terminal. Name Actual capacity Modelled capacity requirement Difference Jakobsberg station 11 7.94 3.06 Used for regression analysis. Some of the layover parking capacity is restricted to non-articulated vehicles, putting a capacity constraint that is not modelled here but could cause asymmetric capacity constraints leading to shortage for layover parking capacity for articulated buses. Possibly used for more driver breaks than the average bus terminal. Name Actual capacity Modelled capacity requirement Difference Kalhäll station 6 2.52 3.48 Used for regression analysis. Layover parking only along the railway track and therefore the usable capacity is not exact. Possibly used for more driver breaks than the average bus terminal. Name Actual capacity Modelled capacity requirement Difference Norrtälje busstation 7 11.23 -4.23 Used for regression analysis. Norrtälje busstation is the main bus terminal in Norrtälje town outside of Stockholm. The modelled layover parking capacity requirement is much higher than the current capacity and the bus terminal is considered by the only operator as well working in that aspect. One possible explanation is that the bus terminal is located only a few minutes from the bus depot, another is that the trips originating in Norrtälje busstation are rather long, in many cases more than one hour one-way, combined leading to a higher share of trips that are preceded by a deadheading. Many of the bus stops in the bus terminal also have very low departure frequency and can possibly be utilised as layover parking. Name Actual capacity Modelled capacity requirement Difference Norsborg T-bana 1 4.74 -3.74 Used for validation only since it’s yellow-rated, while the model predicts it as severely congested. The bus terminal offers only a driver pause facility. The bus terminal is served by three bus lines, two of which run all day and one runs only during peak hours and solely in the main flow direction. One of the two all-day bus lines usually turn around in a few minutes and the other all-day bus line is very frequent and from the timetable it is not possible to tell how often the need for layover parking arises. Name Actual capacity Modelled capacity requirement Difference Nynäshamn station 5 2.79 2.21 Used for regression analysis. Located at the very far end of one of the commuter train service lines of Stockholm this is a small bus terminal and all layover parking is done kerbside. It is not impossible that the practical capacity of the kerbside layover parking is lower than estimated. Name Actual capacity Modelled capacity requirement Difference Rimbo station 6.65 3.51 3.14 Used for regression analysis. Located in a small town outside of Stockholm with a few but long bus services. It is not impossible that the practical capacity of the kerbside layover parking is lower than estimated.

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Name Actual capacity Modelled capacity requirement Difference Ropsten T-bana 6 9.83 -3.83 Used for regression analysis. Serving the island of Lidingö and a few city lines this is a busy bus terminal with approximately 600 departing trips per day. The only operator in the bus terminal described it as operating slightly below maximum layover parking capacity while the model predicts it to be congested. This might be because all lines in this bus terminal are operated with a concept of frequent change of driver; which is the standard method for giving the drivers their breaks in the city but also heavily utilised on Lidingö. Name Actual capacity Modelled capacity requirement Difference Sollentuna station 11.25 6.84 4.41 Used for regression analysis. The difference could be exaggerated here, since some of the layover parking capacity comes from kerbside parking which could have lower capacity in practice than assumed, meaning that the assumed usable capacity of eleven buses could be lower in practice. This bus terminal was rated orange by two operators but rated red by the main operator in the bus terminal. There is also a possibility that this bus terminal is used for more driver breaks than the average bus terminal, leading to a higher requirement of layover parking. Name Actual capacity Modelled capacity requirement Difference Tekniska Högskolan T-bana 16 5.60 10.40

Used for validation only. Located within the city this bus terminal serves three regional lines with high frequency and some 200 daily trips origin at Tekniska Högskolan on a winter weekday. The bus terminal is red rated while the model predicts it to be operating far below maximum layover parking capacity. This was expected for this particular bus terminal since it is used as a daytime bus depot for buses belonging to the Norrtälje bus depot almost seventy kilometres away.

As shown in table 6-1, there are two evident sources of error shown in this part. It seems that bus terminals where operators have the choice of planning driver breaks are underestimated by the selected model, which is missing such a variable. It is also implied that the capacity of kerbside layover parking may be overestimated by the selected model, or at least in the bus terminals discussed in table 6-1. Correctly approximating the capacity of kerbside layover parking depends on several factors. One likely being average vehicle length, a factor that is not the same in all bus terminals since the mix of vehicles in bus terminals differ. Articulated buses (approximate vehicle length is eighteen metre) require more space than two or three axle rigid buses (two axle buses are normally twelve metre and three axle buses are up to fifteen metre in length). This does not mean that the assumed average length requirement of twenty metres is erroneous in general but rather that the assumed value is not correct in certain bus terminals. Nor does this confirm the value of 20 metres in general, but the result gives a hint that this is a possible source of error.

Another possible source of error is if bus stops can be used for layover parking. In bus terminals with few departures per bus stop during non-peak it is intuitively likely that bus stops with infrequent departures can be used for layover parking. There are drawbacks with this strategy, such as a likely longer walking distance to a break room and especially in the case of the shorter pauses it is harder for the driver to use the bus as a warm shelter during the break if the bus is parked at the departure stop, due to passengers waiting right outside the bus. The latter issue can be amended by using a different bus stop for parking than boarding. Further discussion on this possible source of error is presented in section 6.3.

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In bus terminals shared between operators there may be agreements on how and when certain layover berths can be used by a certain operator. Different operators may also have different conditions for optimisation of vehicle utilisation due to differences in interlining possibilities or turn-around times in the same bus terminal since network characteristics differ. Effects of agreements on distribution of layover parking capacity between operators could not be identified with the selected method.

In addition to these sources of error the interpretation of the questionnaire questions may differ between respondents. It is not easy to only consider one single aspect of a matter when asked about it, even if the question asked about the matter is very specific. While the questionnaire explicitly only asked about layover parking capacity it could have been interpreted as also including the traffic flow around the layover parking area, since this in a sense is affecting the utilisation of the available space. In the questionnaire the responder was asked to leave a motivation for the answer but as mentioned in the previous section, very few motivations were provided, and none were detailed. This lowers the confidence in the responses’ precision.

6.2. Comparison

There are several rule-of-thumb models for predicting layover parking capacity requirement in circulation among the actors of the industry. Two such models are:

x Twice the number of turning lines (figure 6-1) x The number of turning lines minus one (figure 6-2)

The definition of a turning line in this section is altered to be a line that, at some point during the day, had a trip originating in the studied bus terminal. This alteration was made due to availability of data. To add the few lines that at some point under the day had a trip terminating in the studied bus terminal but at no point during the day had at least one starting trip would make a very small difference compared to local differences of what constitutes a bus line in the timetable. In some areas of Stockholm several routes sharing a common corridor constitutes a common bus line while in other areas of Stockholm a unique line number is assigned to routes in the same corridor but with different length.

In both plots the grey line represents perfect prediction of the current situation in the bus terminal, which is assumed to be desirable for the bus terminals marked as orange. Bus terminals marked as red should be placed above the grey line and yellow should be below the grey line. Both of these models incorporated the number of turning lines as their sole independent variable, the only differences were the coefficient for the independent variable and the constant.

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Figure 6:1: Rule of thumb model 1

In figure 6-1 above it is evident that the absolute majority of bus terminals rated orange was greatly overestimated by this model, why it was discarded without further analysis. In figure 6-2 below the overestimation of bus terminals rated orange is less evident, but results seem scattered and there was no clear separation between the bus terminals rated differently. The required layover parking capacity was evidently greatly overestimated in most bus terminals by this model.

Figure 6:2: Rule of thumb model 2

The models shown in figures 6-1 and 6-2 seemed to greatly exaggerate the layover parking requirement in most bus terminals. The simplicity of both these models was their greatest strength while their performance was wanting compared to the selected model (section 5.3.2). Using least squares method, the setup for the models shown in figures 6-1 and 6-2 was calibrated for a slightly better fit: see table 6-2 and figure 6-3.

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Table 6-2: Calibration for "rule-of-thumb" models y = ax1 + b y: predicted layover capacity requirement x1: number of starting lines a 0.398 b 2.097 R2 0.453

Figure 6:3: Evaluation of the calibrated "rule-of-thumb" models

As expected, the calibrated model did not exaggerate the orange-rated bus terminals and the issue of non-separation of differently rated bus terminals persisted. R square is below 0.46, which was well below the R square of the selected model (0.72), with adjusted R square on 0.46 and 0.69, respectively. Figure 6-4 shows the relation between layover parking capacity and starting lines, showing some vertical separation between bus terminals of different rating but it was also obvious that this variable alone was insufficient to predict a bus terminal’s rating.

Figure 6:4: Layover parking capacity vs number of starting lines for different categories of bus terminals

R² = 0.4534

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Another intuitive model would be to use the number of bus stops in the bus terminal for modelling layover parking capacity. In table 6-3 and figure 6-5 the results of linear regression analysis of this model are presented.

Table 6-3: Calibration for the rule of thumb model based on number of stops y = ax1 + b y: predicted layover capacity requirement x1: number of bus stops a 0.803 b 0.218 R2 0.381

Figure 6:5: Evaluation of the calibrated rule of thumb model based on number of stops

Using bus stops as sole predictor variable for layover parking capacity requirement provided a worse fit than using the number of bus lines for the studied bus terminals. Given figure 6-5 above it was easy to suspect that the number of bus stops and rating for layover parking was not related. Figure 6-6 confirms this suspicion, there is little separation vertically between bus terminals of different rating. If this was a reliable predictor of bus terminal rating the bus terminals of different rating would form diagonal sectors in the plot.

Figure 6:6: Layover parking capacity versus bus stops in bus terminals of different rating

R² = 0.3807

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6.3. Work Limitations

The most obvious source of error was the highly subjective level of service description given from the operators. The very same bus terminal was not necessarily given the same rating by all operators using it. This can have multiple reasons, such as:

x Different volume

x Different intentions for use

x Different operating model

x Personal opinion

x Different number of layover berths allocated for different operators sharing a bus terminal

One of several examples of the two first was Sollentuna station, a suburban bus terminal north of Stockholm on the Märsta and Uppsala commuter train lines. Sollentuna station served as a major hub for one operator, with major interlining possibilities and usually connection from trains arriving from Stockholm or departing towards Stockholm. This operator rated the bus terminal as red, for not providing enough layover parking capacity. The Sollentuna station bus terminal was also served by other operators, as a far end of their lines, likely with much shorter turn-around times or alternatively with only one bus laying over in the bus terminal at a time. It was also possible that their timed connection was with trains running in the opposite direction or that no such timed connection existed for their lines in that particular bus terminal, since timed connection usually only happens at one point along a route. Both of these operators marked the bus terminal as orange, operating on the layover parking capacity limit.

One operator may consider a bus terminal congested since the intention was to use it for relatively more driver breaks with the bus waiting for the driver to finish the break. This model of operation can be assumed to increase personnel satisfaction and minimises the risk of error due to vehicles being misplaced, which can occur due to delays or the human factor but increases the requirement for layover parking (and also decreased operational efficiency which is outside the scope of this thesis). Another operator may deem the same bus terminal’s layover capacity sufficient due to a practice of not having the vehicle wait with the driver for the same period of time (instead opting for giving the vehicle to another driver who just finished the driver break), leading to relatively fewer cases where the driver keeps the same vehicle after a driver break. A third operator may only use the bus terminal for driver pauses. This leads to uncertainty about the grading of the bus terminals layover parking capacity level of service. Lastly the opinion of the person responding to the questionnaire matters since the evaluation was done subjectively. Different persons read the question differently and thus responded differently and also had different definitions of what level of problem solving was acceptable when doing operational planning without describing something as problematic.

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Another source of error was that the operators did not motivate their response in the questionnaire, with few exceptions where only short non-detailed comments were given. This was possibly due to the ongoing procurement processes and the operators were extra careful not to share any information that could be deemed business sensitive, and time constraints since there was no budget to compensate the operators for their cooperation in this study.

The choice not to run regression on all possible combinations of all variables thinkable with all manipulations imaginable and instead relying on a process of eliminating identified groups of variables was suboptimal, however it turned into a necessity given the method of using Microsoft Excel for calculations. Microsoft Excel is a functional tool for many calculations but could not load workbooks filled with several millions of regressions to be run on the fly upon opening Microsoft Excel on a modern laptop computer. Instead, the regressions had to be limited to a few hundred thousand in each workbook and split across approximately 60 workbooks even after the process of eliminating most of the variables, each workbook taking up to an hour to load and process while consuming more than ten gigabytes of RAM. The processing of the workbooks filled with models for regression analysis proved to be the bottleneck of the chosen technical method and, in hindsight, should have been done using Python. The decision to use Microsoft Excel for data processing and Python only for building the workbooks was based on the ease of visualising data using Microsoft Excel. This proved to be suboptimal upon realising that Microsoft Excel was essentially unable to open and work with workbooks containing billions of formulas, the majority of which being volatile, even on a modern laptop. The main limitation was the high use of RAM, which often exceeded the available amount (16 gigabytes) and turned the system unresponsive. Using compression of data stored in RAM, the system still stalled when Excel utilised in excess of 50 gigabytes of RAM.

The timetable for only one day was studied. The selected day was, as stated before, selected with care and was a representative normal weekday for the spring, autumn and winter timetables of 2019. Studying several timetable periods were deemed non-feasible. The main source of error in the input data were the quality and conformity of responses from the questionnaire. Adding another date from another timetable period would require the same questionnaire being used for that timetable period and securing that the respondent correctly remembers how the situation was when planning that specific timetable period.

The method did not allow for inclusion of any variables describing differences in peak characteristics between bus terminals. Some bus terminals may have a longer sustained peak interval than other bus terminals and the selected methodology did not allow checking if this had any correlation on the requirement for layover parking. The only way the methodology handled this was by comparing models using different peak intervals, which revealed that the shortest studied peak interval had the best correlation with layover parking capacity requirement, on average. Studying even shorter time peak duration intervals was not done due to project time constraints.

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The impact of connections was simplified in this study compared to the real world. In this study it was assumed that connection only existed to or from one line (for rail services several lines running in a common corridor were assumed to work as one line) running in one direction. All possible connections were then treated equal. In reality this is not always the case. For rail services with a cyclic schedule there are usually trains running at fixed intervals during a large part of the day and additional trains added in between these trains. Many bus lines connect to every second or every third train during off-peak hours, e.g. trains run every fifteen minutes and a connecting bus line runs every thirty minutes, another bus line with the same frequency connects with the other trains, and lastly a third bus line connects to every train (thus running every fifteen minutes). In this simple example two bus departures connect to every train.

During rush hour additional trains and buses are added between every off-peak departure. This leads to three buses connecting to each of the base-line service trains and one bus connects to the peak service trains. By this method a cyclic timetable is retained at the price of asymmetric load on the public transport system. Since the maximum requirement for layover parking capacity is not expected during the interval with the highest bus service intensity but offset to it, the impact of unequal distribution of connecting bus departures to every relevant arriving train is in this study assumed to equal to all bus terminals.

To further complicate matters, the layover parking capacity requirement is also affected by the time window between the timed connection to the rail service and the timed connection from the rail service, impacting if it is possible to have driver pauses in that time window or not. If not, the requirement for layover parking capacity may increase due to more vehicles required to operate the bus services. The operator can change driver more frequently to alleviate this, at the possible expense of decreased resilience to service abnormalities. The time window between departing and arriving connections may change between timetable periods and cannot be assumed to remain unchanged from one year to another.

Bus terminals connected to rail services should also be dimensioned with rail replacement bus services in mind, this is not explicitly included in the study and is a source of error. It is not possible to assess whether the response to the questionnaire included this kind of extra capacity needed, since the responses were subjective. It is important to note that not only termini of the rail service lines require spare layover capacity since replacement bus services often replace segments of the rail services.

Since many bus terminals are interconnected with bus lines running from one bus terminal to another with few bus terminals being more or less isolated from other bus terminals it is likely that capacity constraints in one bus terminal impact other bus terminals in the network vicinity. It is possible that at least some of the bus terminal layover parking capacity ratings from the questionnaire are affected by this, especially if there are several operators acting independently in the bus terminal. However, given the sample size of this study, it is impossible to draw any conclusions regarding this.

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Another limitation of this thesis study was the extend of using bus stops in bus terminals for layover activities. Section 6.1 discussed that the model did not provide good predictions for the layover requirements for some bus terminals due to its limitation in incorporating the possibility of using bus stops as layover parking berths. The analysis performed in this study for bus operations data has used public timetables to create more meaningful information or variables. This type of data do not reveal the details of operator’s practices inside the bus terminals, which made it impossible to investigate which bus stops are actually being used for layover activities.

Although timetable headways can indicate the extent of how long a certain bus stop will be unoccupied and possibly be available for a bus layover activity, this requires acquiring information from operators if they have been indeed using a particular bus stop for layover activities and if that effected their evaluation for the bus terminal in the survey study, and this is a question which operators likely would deem to be business sensitive and too complicated to be answered.

A possible approach to explore the extend of using bus stops in bus terminals for layover activities is to analyse Automatic Vehicle Location data (AVL) or the planned vehicle blocks for buses using a certain bus terminal. Together with the survey study results, one could deduce answers on how adequate the layover parking zone capacity is, and if it was deemed to be adequate due to utilising bus stops in the bus terminal as layover parking berths.

The case study discussed that one approach to adapt to layover parking capacity limitations in bus terminals is to plan break or pause activities in locations outside bus terminals (see section 3.4). Although the study successfully managed to acquire information on layover parking locations outside of bus terminals through personal interviews rather than via the survey study, the final model incorporated a pause factor that only recognises pause activities inside of bus terminals. To incorporate this additional factor dummy-coding of a categorial variable would be required, which would generate probably two or three additional variables that need to be inserted into a regression analysis for a sample size of only twenty-eight data points, and this can create overfitting of the data and failures in capacity predictions.

Another adaptation technique that the case study discussed in section 3.4 is the change of drivers. This instrument accounts for some of the predictions that the regression model failed to account for. Change of drivers may have an impact on how operators evaluate different bus terminals’ layover parking capacity, especially since different operators incorporate the change of drivers instrument differently in terms of tendencies in their planning techniques. For example, operators can be expected to maximise vehicle utilisation during peak hours, and a possible way to accomplish this is to extensively utilise the change of driver tool when a driver needs to have a pause or break.

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Using the change of driver tool to maximise vehicle utilisation outside of peak hours can increase vehicle availability for the maintenance workshop but decreases resilience to service abnormalities. Operators can be expected to balance these factors, and many other factors, in different ways. Operators may balance operational factors differently between different contracts to optimise the cost of penalty fines versus operational cost. Similar to other presented work limitations for this project, it is impossible to deduce such information from timetables, and it is very probable that operators will find such information too sensitive to be shared.

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7. CONCLUSION

The lack of literature focused on the proper dimensioning of layover parking capacity in bus terminals reveals that this problem area is newly emergent. Although the literature helped in understanding how layover parking capacity limitations can have a negative effect on the quality of bus services, efficiency of rail services and on public transport systems as a whole, yet it rarely discussed possible solutions to overcome these limitations.

This created difficulties to find a starting point for this research, in addition to the complexity of the factors contributing to the requirement for layover parking capacity and the creation of their interaction terms. This lack of literature shifted this study to be more practical-oriented and that the majority of the theories and conclusions exhibited in the case study of Stockholm stemmed from numerous interviews with experienced personnel with bus scheduling, strategic planning, traffic consultancy and bus-driving backgrounds.

In the majority of bus terminals’ investments around the world, a large share of the land acquisition that is dedicated for a bus terminal will be appointed to bus layover activities. Considering the continuously rising prices in the real estate market, the increased population densities, and the attractiveness of bus terminals’ that are near key locations, it is of high importance to conduct a study that provides an optimum estimation for the requirements of layover parking without jeopardising the future development of bus services. The study should consider the effects of interlining of bus lines, the usage of optimisation tools for bus scheduling, the arrival pattern of buses and the impact of having intermodal connections at a bus terminal.

The ambition to have a lively and dynamic urban space around bus terminals should not compromise bus layover requirements. Presently traffic planners find it hard to motivate to the politicians the long-term space required for a layover parking facility that will not be used within fifteen years from now. The Public Transport Administration in Stockholm County is among the pioneers to formally address the necessity to have a strategic tool that could form a basis for negotiations with decision makers for space that should be dedicated for layover parking.

Limitations in available data were a major bottleneck in this study. The larger share of this work’s limitation was due to data-sensitivity and a lot of efforts were spent on deducing information from publicly available data and designing and executing a survey study with a rather generalised questionnaire. The theories that this study presented on the effect of interlining, service attributes and network attributes have a rationale, where having access to AVL data collected from buses operating within the network would have revealed more accurate and detailed estimations for the effect of these properties.

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In reality, the interaction between layover parking demand and the available capacity cannot be investigated with on-time performance measurements since public transport operators must and can maintain the required service quality. Instead, public transport operators will need to add further instruments that are suboptimal for an efficient service and incorporates extra operational costs. Given that these details of operations are not known during bus terminal planning stages, the model provided in this study can be a helpful tool in the decision-making process.

Understanding the complications of bus operations when interlining is incorporated in the scheduling process has formed the first step towards understanding how layover demand is accounted for. Although the model includes variables that can be generalised for other networks than the one in Stockholm county, the interlining effect along with driver’s union regulations makes some variables unique for Stockholm and needs calibration if intended to be used for layover capacity predictions in other networks.

When using the model, it is important to acknowledge that it does not cover all aspects of what creates a requirement for layover parking in bus terminals.

x It is likely that bus terminals that should facilitate driver breaks require additional capacity, especially and to a greater extent if it should be possible for the bus to stay inactive while the driver has a break.

x Bus terminals are normally not isolated but function as a part of the network to which they belong, and the quality of service of surrounding bus terminals should therefore be taken into account.

x Timed connections affect layover parking capacity requirement and also consider the time window between connections in either direction. The model compensates to some degree for the average effects of timed connections.

x If located by a rail line, capacity for rail replacement bus services shall be considered.

x The model handles large bus terminals in a non-reliable fashion.

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7.1. Future Research

This study seems to be a first attempt on describing the layover parking requirements in bus terminals using regression analysis on the situation in existing bus terminals. During the study, several aspects were identified that could not be fully covered and it would be advisable to study these aspects closer, the list continues on the next page.

x From the study it is obvious that layover time is critical when identifying layover parking capacity requirement in a certain bus terminal. In the study two variables were used to describe differences in layover time ratios (pause and break factors from section 4.2.1), however only the pause factor came to use in the selected model, balanced by another variable describing the exact same aspect but without the pause factor, indicating that the pause factor may have exaggerated the impact of the aspect it is describing. Further research is recommended to improve how layover time can be generalised by using moderators based on data which also is available during early planning.

x The impact by rail service replacement buses on layover capacity requirement was not studied and it is advisable to research how it should be handled in bus terminals.

x The model was created for Stockholm and it was not tested against any bus terminals outside the greater Stockholm area. Further study is required to validate or recalibrate it for use in other areas.

x Redoing the study using a larger data set (for regression and for validation) with an improved and more reliable quality of service indicator and testing all possible variable combinations with more variables would after careful selection probably result in a model with higher adjusted R square and better prediction of the bus terminals in the validation data set with relatively fewer outliers. If redoing this study, it is advisable not to use Microsoft Excel to handle lists with several million regression models, calculated by Microsoft Excel on loading of the workbook. That turned out to be a major bottleneck in this study when the lists had to be split between almost 100 workbooks, each taking up to an hour to load and process in Microsoft Excel.

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9. APPENDICES

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APPENDIX A

Kerbside parking is an estimation of all available layover parking best described by its length. Starting lines are the number of bus lines with at least one trip originating in the bus terminal. Connections are the maximum identified number of connections per hour on the route identified to be the most likely timed connection for departing bus services.

Akal

la T

-ban

a

Alvi

k T-

bana

Bran

dber

gen

cent

rum

Bro

stat

ion

Brom

map

lan

T-ba

na

Dand

eryd

s sju

khus

T-b

ana

Fars

ta S

tran

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atio

n

Fars

ta T

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Fitt

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a

Flem

ings

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ion

Fruä

ngen

T-b

ana

Gullm

arsp

lan

T-ba

na

Gust

avsb

erg

cent

rum

Hallo

nber

gen

T-ba

na

Halls

tavi

k st

atio

n

Terminal stops 4 3 3 2 8 12 1 5 5 0 4 17 7 4 2 Street stops 0 0 2 0 0 0 4 2 0 2 2 3 0 1 0 Dedicated layover berths 0 0 0 0 0 21 0 0 0 0 0 14 0 0 7 Out-of-use bus stops 2 1 1 0 1 0 0 0 0 0 0 1 2 0 0 Kerbside parking (m) 60 40 0 20 210 50 0 40 0 0 70 0 20 20 0 Starting lines 4 3 7 0 17 31 3 6 5 1 6 25 10 3 6 Strictly continuing lines 3 0 3 2 2 3 3 2 2 8 1 4 5 2 0 Connections/h (max) 10 30 6 4 24 12 12 12 12 8 12 30 12 10 - Driver pause No Yes No No Yes Yes Yes Yes Yes No Yes Yes Yes No Yes Driver break No Yes No No Yes Yes No No No No No Yes Yes No Yes

Hallu

nda

T-ba

na

Hand

en st

atio

n

Hudd

inge

sjuk

hus

Hudd

inge

stat

ion

Högd

alen

T-b

ana

Jako

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rg st

atio

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Jord

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ion

Järn

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Kallh

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tatio

n

Karo

linsk

a sj

ukhu

set n

orra

Kist

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Kung

ens K

urva

Kung

säng

en st

atio

n

Kärr

torp

T-b

ana

Köpm

anga

tan

Terminal stops 5 8 7 9 3 10 2 2 3 7 5 6 3 2 10 Street stops 0 0 0 0 2 3 2 0 1 0 1 0 0 2 0 Dedicated layover berths 0 0 0 3 2 7 0 0 0 6 0 0 1 0 0 Out-of-use bus stops 0 0 0 0 0 0 0 1 1 4 0 0 1 0 0 Kerbside parking (m) 85 320 20 100 0 80 60 0 100 0 100 40 0 40 0 Starting lines 2 7 4 7 4 11 4 4 5 8 12 4 3 2 6 Strictly continuing lines 6 5 6 1 3 0 0 0 0 3 5 4 1 1 9 Connections/h (max) 6 6 8 8 12 8 4 2 8 - 10 - 6 9 6 Driver pause Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes No No Driver break No Yes No Yes Yes Yes No No Yes No Yes No Yes No No

Lilje

holm

en T

-ban

a

Mär

sta

stat

ion

Nac

ka F

orum

Nor

rtäl

je b

usst

atio

n

Nor

sbor

g T-

bana

Nyn

äsha

mn

stat

ion

Ode

npla

n T-

bana

Orm

inge

cent

rum

Rim

bo b

usst

atio

n

Rönn

inge

stat

ion

Rops

ten

T-ba

na

Skär

holm

en T

-ban

a

Slus

sen

T-ba

na

Solle

ntun

a st

atio

n

Soln

a ce

ntru

m T

-ban

a

Terminal stops 11 6 10 11 3 4 7 5 6 4 8 4 18 9 6 Street stops 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 Dedicated layover berths 4 0 0 7 0 0 0 0 0 0 0 0 12 6 0 Out-of-use bus stops 1 0 0 0 1 0 2 0 1 0 0 0 0 0 0 Kerbside parking (m) 0 240 0 0 0 100 0 25 113 40 120 42 0 105 54

107

Lilje

holm

en T

-ban

a

Mär

sta

stat

ion

Nac

ka F

orum

Nor

rtäl

je b

usst

atio

n

Nor

sbor

g T-

bana

Nyn

äsha

mn

stat

ion

Ode

npla

n T-

bana

Orm

inge

cent

rum

Rim

bo b

usst

atio

n

Rönn

inge

stat

ion

Rops

ten

T-ba

na

Skär

holm

en T

-ban

a

Slus

sen

T-ba

na

Solle

ntun

a st

atio

n

Soln

a ce

ntru

m T

-ban

a

Starting lines 13 19 1 24 5 4 7 8 5 2 14 6 35 12 4 Strictly continuing lines 1 2 49 0 2 2 7 8 1 1 0 2 12 1 4 Connections/h (max) 24 4 - 12 6 2 30 - 6 6 12 12 - 8 10 Driver pause Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Driver break Yes No No Yes No Yes Yes No Yes No Yes Yes Yes Yes Yes

Soru

nda

Spån

gbro

Spån

ga st

atio

n

Sund

bybe

rg st

atio

n

Söde

rtäl

je c

entr

um st

atio

n

Söde

rtäl

je h

amn

stat

ion

Söde

rtäl

je S

yd st

atio

n

Tekn

iska

Hög

skol

an T

-ban

a

Tele

fonp

lan

T-ba

na

Trol

lbäc

ken

cent

rum

Tulli

nge

stat

ion

Tum

ba st

atio

n

Tyre

sö ce

ntru

m

Täby

cent

rum

stat

ion

Uni

vers

itete

t T-b

ana

Upp

land

s Väs

by st

atio

n

Terminal stops 4 8 2 8 3 2 3 2 4 2 9 7 9 3 9 Street stops 0 0 0 0 0 0 0 2 1 4 0 0 0 0 0 Dedicated layover berths 0 5 0 0 0 0 12 2 0 0 3 0 0 0 6 Out-of-use bus stops 0 0 0 0 1 0 2 0 0 1 1 2 2 0 0 Kerbside parking (m) ? 0 60 60 0 40 40 0 0 0 40 0 65 100 70 Starting lines 3 10 3 19 1 0 5 2 3 7 16 9 6 2 15 Strictly continuing lines 1 3 1 3 6 6 9 2 7 1 1 4 4 10 0 Connections/h (max) - 8 8 6 6 6 12 12 - 8 8 - 6 12 8 Driver pause Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Driver break No Yes Yes Yes No No Yes No No No Yes Yes Yes No Yes

Valle

ntun

a st

atio

n

Välli

ngby

T-b

ana

Väst

erha

ning

e st

atio

n

Åker

sber

ga st

atio

n

Årst

aber

g st

atio

n

Älm

sta

buss

tatio

n

Älvs

jö st

atio

n

Ösm

o ce

ntru

m

Ösm

o st

atio

n

Öst

ertä

lje st

atio

n

Terminal stops 4 6 5 6 3 3 12 2 6 5 Street stops 0 0 0 1 2 0 0 0 0 0 Dedicated layover berths 0 2 0 18 0 4 0 0 0 0 Out-of-use bus stops 0 0 0 0 0 0 0 0 1 0 Kerbside parking (m) 60 40 60 0 20 0 20 20 0 80 Starting lines 5 7 6 11 1 3 8 2 0 3 Strictly continuing lines 3 0 1 1 2 0 2 4 5 3 Connections/h (max) 6 16 6 6 16 2 16 2 2 6 Driver pause Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Driver break No Yes Yes Yes No Yes Yes Yes Yes Yes

Excluded bus terminal Reason Bro station No trips originating from the bus terminal. Fridhemsplan Not a proper bus terminal and located in the city. Uncertainty about layover capacity. Gustavsberg centrum No response in survey. Hallstavik station Also being a bus depot, the layover capacity is exaggerated. Nacka Forum Uncertainty about layover capacity. Orminge centrum No response in survey. Rönninge station No response in survey. Sorunda Spångbro Uncertain layover capacity. Södertälje Syd station No trips originating from the bus terminal.

108

APPENDIX B

109

Bussterminal Röd Orange Gul Grön BBA-rast

BBA-paus Kommentarer

Flemingsbergs station Fridhemsplan Fruängen Gullmarsplan södra Gullmarsplan norra Gustavsbergs centrum Hallonbergen Hallunda centrum Handenterminalen (temporära)

Huddinge sjukhus Huddinge station Högdalen Jakobsberg station Jordbro station Kallhäll station Karolinska sjukhuset norra

Kista centrum Kungsängen station Köpmangatan Liljeholmen övre Liljeholmen nedre Märsta station Norrtälje busstation Norsborg Nykvarn station Nynäshamns station Odenplan Orminge centrum Rimbo busstation Ropsten Rotebro station (v+ö) Rönninge station Skärholmen Slussen (Stadsgården) Södermalmstorg Sollentuna station Solna centrum Spånga station Spångbro Sundbyberg station Södertälje centrum Södertälje hamn Södertälje syd Tekniska högskolan (regionbussterminalen)

Telefonplan Trollbäckens centrum Tullinge station Tumba station Tyresö centrum

110

Bussterminal Röd Orange Gul Grön BBA-rast

BBA-paus Kommentarer

Täby centrum Ulriksdals station Universitetet Upplands Väsby station

Vallentuna station Vällingby Västerhaninge station Åkersberga station Årstaberg Älmsta Älvsjö station Ösmo station Ösmo centrum Östertälje station

Fyll gärna på om någon terminal saknas.

111

Ytterligare rast- eller pausmöjlighet finns vid nedanstående platser (lista alla som används, se exempel nedan):

Plats Betjänar hållplatser BBA-rast

BBA-paus

Kommentarer (förarbyte för passerande turer för rast och/eller paus? restriktioner i nyttjande?)

Namn på plats, exempelvis en bussdepå.

Lista över hållplatsnamn som är relevanta i depåns närhet. Till exempel avlösningshållplatser eller ändhållplatser varifrån det är en kort tomkörning till sagda bussdepå.

X X Beskriv exempelvis om det sker förarbyten för rast eller paus vid de betjänta hållplatserna.

Namn på plats, exempelvis en ändhållplats där det finns toalett som duger för BBA-paus.

Namn på plats, exempelvis en ändhållplats.

X Beskriv till exempel om det finns något som hindrar nyttjande eller som gör att ni hellre planerar paus på annan plats.

Namn på plats, exempelvis en bussterminal eller en bussdepå.

Skriv namn på ändhållplatser som är närbelägna funktionsmässigt och leder till att det skapas ett behov av rast/paus i terminalen.

X Beskriv om det till exempel är så att dessa tomkörningar endast görs av en viss linjevariant som inte har tillgång till motsvarande faciliteter vid andra ändhållplatsen.

112

Plats Betjänar hållplatser BBA-rast

BBA-paus

Kommentarer (förarbyte för passerande turer för rast och/eller paus? restriktioner i nyttjande?)

Fyll på med fler även om de tomma rutorna tagit slut, det är viktigt att inga faciliteter utelämnas.

113

APPENDIX C

Questionnaire Part B results Bussterminal Keolis Nobina Arriva TransDEV BBA-rast BBA-paus Akalla RED

Alvik Orange X

Barkarby station (temporära) Yellow X

Brandbergen Orange

Brommaplan Orange X Danderyds sjukhus (inkl. Mörby station) RED X

Farsta centrum RED Orange X

Farsta strand (alla hållplatser) RED RED X

Fittja RED RED RED X

Flemingsbergs station RED

Fridhemsplan RED X

Fruängen RED X

Gullmarsplan södra Orange RED RED X

Gullmarsplan norra Orange X

Gustavsbergs centrum

Hallonbergen RED

Hallunda centrum Yellow

Handenterminalen (temporära) RED

Huddinge sjukhus RED X

Huddinge station Orange X

Högdalen Orange X

Jakobsberg station Orange Orange Yellow X

Jordbro station Orange X

Järna station (NY!) Yellow

Kallhäll station Orange X

Karolinska sjukhuset norra Orange Orange X

Kista centrum RED Orange X

Kungens Kurva (NY!) Orange

Kungsängen station RED

Köpmangatan RED

Liljeholmen övre RED

Liljeholmen nedre RED RED X

Märsta station RED Yellow

Norrtälje busstation Orange

Norsborg Yellow

Nykvarn station

Nynäshamns station Orange X

Odenplan Orange RED X

Orminge centrum

Rimbo busstation Orange X

114

Ropsten Orange X

Rotebro station (v+ö) Green X

Rönninge station X

Skärholmen RED X

Slussen (Stadsgården) RED X

Södermalmstorg RED X

Sollentuna station Orange RED Orange X

Solna centrum RED X

Spånga station RED X

Spångbro Orange

Sundbyberg station RED X

Södertälje centrum RED X

Södertälje hamn Orange X

Södertälje syd Orange X Tekniska högskolan (regionbussterminalen)

RED RED X

Telefonplan Orange X

Trollbäckens centrum RED

Tullinge station RED X

Tumba station Orange X

Tyresö centrum RED X

Täby centrum Orange X X

Ulriksdals station X

Universitetet Orange X

Upplands Väsby station Yellow Yellow X

Vallentuna station Orange X

Vällingby RED RED X

Västerhaninge station Yellow X

Åkersberga station Green X

Årstaberg Orange X

Älmsta Orange

Älvsjö station RED X

Ösmo station Yellow X

Ösmo centrum Orange X

Östertälje station Yellow

Bro stn RED

115

APPENDIX D

In this appendix the results of the previously conducted survey studies are presented. The first, conducted in 2014:

Bussentreprenör Bussterminal Förvaltare Upphandlingsetapp bussterminal

Trafikområde (Avtalsområde)

Problemgrad: (1) Inget/lugnt, (2) Litet/hanterbart, (3) Stort/akut

Alvik Bromma, Västerort 3

Brommaplan Bromma 3

Brommaplan Kent Stoltz E20 SSSB block 4 Bromma, Västerort 2

Danderyds sjukhus (Danderyd terminal)

Kent Stoltz E20 Norrort block 2 Norrort 3

Hallonbergen Solna/Sundbyberg 3

Hallonbergen Kent Stoltz E20 SSSB block 3 Bromma, Västerort 2

Karolinska sjukhuset Solna/Sundbyberg 3

Kista centrumSollentuna

3

Odenplan Sundbyberg 3

Ropsten Ersättningstrafik Lidingö

3

Sollentuna station Sollentuna 3

Sollentuna station (Sollentuna C)

Kent Stoltz E20 SSSB block 1 Norrort 2

Solna centrum Solna 3

Spånga station Kent Stoltz E20 SSSB block 1 Bromma, Västerort 3

AR

RIV

A

A

RR

IVA

AR

RIV

A

A

RR

IVA

A

RR

IVA

Kartläggning av terminaler från bussentreprenörs perspektiv

116

Spånga station Bromma, Västerort 3

Sundbyberg Solna/Sundbyberg 3

Vällingby Bromma, Västerort 3

Vällingby Kent Stoltz E20 SSSB block 2 Bromma, Västerort 3

Åkersberga station (Åkersberga terminal)

Kent Stoltz E20 Norrort block 2 Norrort 2

City Mikael Degerman E22 Innerstaden/Lidingö Innerstaden

Fridhemsplan Mikael Degerman E22 Innerstaden/Lidingö Innerstaden 3

Fridhemsplan (S:t Eriksg/Flemingg) Mikael Degerman E22 Innerstaden/Lidingö Innerstaden

Karolinska sjh Kent Stoltz E20 SSSB block 1 Innerstaden 1

Odenplan Mikael Degerman E22 Innerstaden/Lidingö Innerstaden 2

Ropsten Mikael Degerman E22 Innerstaden/Lidingö Innerstaden 3

Slussen/Södermalmstorg Innerstaden 3

Handenterminalen (kommande)

Ulrika Persson E23 Ty Hand Nynäs Handen 3

Handenterminalen (tillfälliga)

Ulrika Persson E23 Ty Hand Nynäs Handen 3

Nynäshamns station Ulrika Persson E23 Ty Hand Nynäs E15 1

Spångbro Ulrika Persson E23 Ty Hand Nynäs E15 2

Västerhaninge station Ulrika Persson E23 Ty Hand Nynäs Handen 2

Ösmo centrum Ulrika Persson E23 Ty Hand Nynäs E15 1

Ösmo station Ulrika Persson E23 Ty Hand Nynäs E15 1

Farsta Centrum Claes Filipsson E19 HBS E19 HBS 3

Farsta Strand Claes Filipsson E19 HBS E19 HBS 1

Fittja Claes Filipsson E19 HBS E19 HBS 2

KE

OLI

S

KE

OLI

S

KE

OLI

S

KE

OLI

SA

RR

IVA

AR

RIV

A

A

RR

IVA

AR

RIV

A

AR

RIV

A

117

Flemingsbergs station Claes Filipsson E19 HBS E19 HBS 1

Fruängen Claes Filipsson E19 HBS E19 HBS 3

Gullmarsplan Claes Filipsson E19 HBS E19 HBS 3

Gustavsberg centrum Claes Filipsson E19 NV E19 NV 3

Hallunda centrum Claes Filipsson E19 HBS E19 HBS 2

Huddinge sjukhus Claes Filipsson E19 HBS E19 HBS 1

Huddinge station/centrum Claes Filipsson E19 HBS E19 HBS 2

Högdalen Claes Filipsson E19 HBS E19 HBS 3

Liljeholmen Claes Filipsson E19 HBS E19 HBS 2

Liljeholmen nedre Claes Filipsson E19 HBS E19 HBS 3

Orminge centrum E19 NV 3

Rönninge Claes Filipsson E19 HBS E19 HBS 1

Skärholmen Claes Filipsson E19 HBS E19 HBS 3

Slussen Claes Filipsson E19 NV E19 NV 3

Telefonplan Claes Filipsson E19 HBS E19 HBS 2

Tullinge station Claes Filipsson E19 HBS E19 HBS 3

Tumba Claes Filipsson E19 HBS E19 HBS 3

Årstaberg Claes Filipsson E19 HBS E19 HBS 3

Älvsjö Claes Filipsson E19 HBS E19 HBS 2

Fittja centrum Claes Filipsson E19 HBS Södertälje (Huddinge/Botkyrka)

2

Järna station Södertälje 1Köpmangatan Södertälje 3

Liljeholmen Claes Filipsson E19 HBS Södertälje(Söderort)

3

Nykvarn Södertälje 1Södertälje centrum Mats Sjölund E13 Södertälje Södertälje 3Södertälje hamn Mats Sjölund E13 Södertälje Södertälje 1Södertälje syd Mats Sjölund E13 Södertälje Södertälje 1Östertälje station Mats Sjölund E13 Södertälje Södertälje 2

Gullmarsplan Claes Filipsson E19 HBS Tyresö(Söderort)

3

Nacka Forum Claes Filipsson E19 NV Tyresö(Nacka/Värmdö)

1

Slussen nedre Claes Filipsson E19 NV Tyresö(Nacka/Värmdö)

3

Trollbäckens centrum Ulrika Persson E23 Ty Hand Nynäs Tyresö(Handen)

1

Tyresö centrum Ulrika Persson E23 Ty Hand Nynäs Tyresö 3

Barkarby station Mats Sjölund E13 Järfälla Bro/Järfälla 2

Bro station Bro/Järfälla 1

Jakobsbergs station Mats Sjölund E13 Järfälla Bro/Järfälla 1

Kallhälls station Mats Sjölund E13 Järfälla Bro/Järfälla 1

NO

BIN

A

NO

BIN

A

NO

BIN

A

NO

BIN

A

N

OB

INA

KE

OLI

S

KE

OLI

S

KEO

LIS

K

EO

LIS

118

Kista centrum Kent Stoltz E20 SSSB block 1 Bro/Järfälla(Sollentuna)

3

Sollentuna station Kent Stoltz E20 SSSB block 1 Bro/Järfälla(Sollentuna)

2

Spånga station Kent Stoltz E20 SSSB block 2 Bro/Järfälla(Bromma)

3

Kungsängens station Mats Sjölund E13 Järfälla Bro/Järfälla 3

Vällingby centrum Kent Stoltz E20 SSSB block 2 Bro/Järfälla(Bromma)

3

Campus Roslagen Norrtälje 2

Danderyds sjukhus Kent Stoltz E20 Norrort block 2 Norrtälje(Norrort)

3

Hallstavik Mats Sjölund E19b Norrtälje Norrtälje 1Norrtälje busstation Mats Sjölund E19b Norrtälje Norrtälje 3Rimbo station Mats Sjölund E19b Norrtälje Norrtälje 2

Tekniska högskolan Norrtälje 3

Åkersberga station Kent Stoltz E20 Norrort block 2 Norrtälje(Norrort) 2

Älmsta Mats Sjölund E19b Norrtälje Norrtälje 1

Allmänt från NOBINA.

Visserligen är det sedan även en begriplig kompromiss, men det är ändå olyckligt när man numera ofta leder cykelbanor genom och tvärs över terminalområden. I många fall är olycksrisken oroväckande hög.

I många fall verkar även kommunerna se terminalormådena som byggbar mark. Det innebär att vi i många fall har väldigt svårt att få gehör för de behov som busstrafiken de facto har.

Vissa märkligheter har även uppstått vid nybyggnationer vilket innebär att terminaler som Liljeholmen, Årstaberg, Älvsjö, Kungsängen, Solna C m.fl. i stort sett har sämre förutsättningar än vad som fanns innan.

Vi kör även ersättningstrafik för pendeltågen och ett stort underskott här är att de hållplatser som finns för ersättningstrafiken finns på plats, men det är ofta så sorgfälligt utfört att det ofta finns problem att angöra dessa.

Vi har även vissa brister på de utomlänsterminaler (Gnesta, Bålsta, Knivsta och Uppsala) som trafikeras, men där är frågan om detta är rätt forum för sådant eller om vi ska ta upp ev. sådana ärenden i något annat forum?

Ett annat stort problem som inte bara gäller vid terminalerna utan överallt är att väghållarna vid ny- eller ombyggnation inte följer Ribuss vilket har lett till alltför smala gator på sina håll med dålig tillgänglighet och skador som följd. Det medför i vissa lägen således att bussar knappt kan mötas och/eller får vänta ut varandra och annan trafik i korsningar. Väghållarna verkar tyvärr även konsekvent bygga bort vändmöjligheter mm. vilket medför problem.

NO

BIN

A

NO

BIN

A

NO

BIN

A

NO

BIN

A

N

OB

INA

Ett stort aber är att ett flertal av bussterminalerna är väldigt gamla och är inte alls anpassade till dagens utökade trafik och nya fordonstyper. I många fall är terminalerna från 70-talet och miljonprogramsåren och vissa fall rentav från 50-talet.

Generellt gäller även i många fall att underhåll, skyltning, linjemålning mm. är mycket eftersatt. Det råder ofta även stora oklarheter om vem som är ansvarig väghållare för terminalerna samt var vi ska vända oss i dylika fall.

119

The second is from 2015:

Terminalstatus (upplevd Obj nr Terminal Ansv. TU/SL Trafikeras av Huvudavtal Övr. trafik Terminaltyp Distrikt Antal linjer Bel.grad

Adelsö

4117 Akalla C Arriva Råsta, Norrort, Kallhäll

E20 block A

Alviksplan Arriva Lunda E16 Ers.trafik Lamell Västra 4 vändande 0 genomgående

ArenastadenArlanda flygplats ArrivaArninge handelsplats Arriva Norrort, Norrtälje E20 block B TerminalgataBarkarby staden/Järfälla

4310 Barkarby stn. Nobina Kallhäll, Norrort E28 Ers.trafik4345 Björksviks brygga Keolis Nacka/Värmdö E19

Brandbergen C Nobina Södertörn E23 LamellBro stn Nobina E28

4118 Brommaplan Arriva Lunda, Mälaröarna

E16 Ers.trafik Lamell Västra X vändande X genomgående

4015 Brunn C Nacka/Värmdö E19 Lamell Östra 5 genomgående4346 Bullandö Keolis Nacka/Värmdö E19

Bålsta stn E28Campus Roslagen Nobina Norrtälje E19B

4591 City / Sergels torg / Centralen / Norra Bantorget Keolis, Arriva, Nobina

City, Stockholm sydväst, Björknäs, Råsta, Norrort, Märsta, Lunda, Kallhäll, Södertörn

E22 Ers.trafik, priv. linjetrafik

Terminalgata Centrala

4300 Danderyds sjh. Arriva, Nobina Norrort, Råsta, Mälaröarna

E20b Ers.trafik, priv. linjetrafik

Ö-terminal Norra

4146 Djursholms torg4343 Eknäs brygga Keolis Nacka/Värmdö E194330 Ellan Nobina Norrtälje E19b4211 Estö Nobina Södertörn E23

4119 Farsta centrum KeolisStockholm sydväst, Södertörn

E19 Ers.trafik Lamell Södra

4135 Farsta strand KeolisStockholm sydväst, Södertörn

E19 Ers.trafik Terminalgata Södra

4006 Fittja C KeolisStockholm sydväst, Södertälje

E19 Ers.trafikTerminalgata med lamell Södra

1185/4579 Flemingsbergs stn. Keolis

Stockholm sydväst, Södertörn

E19 Ers.trafik ? Södra

Fridhemsplan Keolis, ArrivaCity, Stockholm sydväst, Råsta, Lunda

E22Ers.trafik, priv. linjetrafik Terminalgata Centrala

4136 Fruängen C Keolis Stockholm sydväst

E19 Ers.trafik Lamell Södra

4334 Glyxnäs Nobina Norrtälje E19bGnesta Nobina Södertälje E27 Ers.trafik

4104 Gullmarsplan norra Keolis Stockholm sydväst, City

E19 Ers.trafik Lamell Södra

4120 Gullmarsplan södra KeolisStockholm sydväst, Södertörn

E19 Ers.trafik Ö-terminal Södra

4013 Gustavsberg C Keolis Nacka/Värmdö E19 Ö-terminal och terminalgata

Östra

HagalundHagastadenHallonbergen C Arriva Råsta, Lunda E20a Ers.trafik Lamell Västra

4338 Hallstavik stn. Nobina Norrtälje E19B UL

4007 Hallunda C Keolis Stockholm sydväst

E19 Ers.trafik Lamell Södra

Hammarby kanalHanden stn. (kommande) Nobina Södertörn E23 Ers.trafik Dockning Södra

4029 Handen stn. (tillfälliga) Nobina Södertörn E23 Ers.trafik Terminalgata Södra4303 Helenelunds stn. Arriva Råsta, Norrort E20 block A Ers.trafik Terminalgata4344 Hemmesta Keolis Nacka/Värmdö E194138 Holmboda

Huddinge centrum Keolis Stockholm sydväst

E19 Ers.trafik Terminalgata Södra

4024 Huddinge sjh. KeolisStockholm sydväst, Södertörn

E19 Ers.trafik Terminalgata Södra

4025 Huddinge stn. Keolis Stockholm sydväst

E19 Ers.trafik Ö-terminal Södra

Häggviks stn. Arriva Råsta E20 block A Ers.trafik Terminalgata4147 Hällsboskolan Arriva

1188 Högdalen C Keolis Stockholm sydväst

E19 Ers.trafik Lamell Södra

4311 Jakobsberg stn. Nobina Kallhäll, Norrort, Märsta

E28 Ers.trafik

4030 Jordbro stn. Nobina Södertörn E23 Terminalgata4148 Jupitergatan Arriva

JärlaJärna stn. Nobina Södertälje E27 Ers.trafik

4312 Kallhäll stn. Nobina Kallhäll, Märsta E28 Ers.trafik4325 Karlslundsplan Nobina Södertörn E23

Karolinska Institutet Arriva, Keolis Råsta, City E20a

4128 Karolinska sjh.et Arriva, Keolis, Nobina

Råsta, Norrort, City, Kallhäll

E20a Priv. linjetrafik Lamell Västra

4121 Kista C Arriva, NobinaRåsta, Norrort, Märsta, Kallhäll, Norrtälje

E20aUL 898, priv. linjetrafik, ers.trafik

Ö-terminal Västra

Knivsta Ar-terminalen

Knivsta stnKulla vägskäl Norrort E20 block B

4324 Kungsängen stn. MTR/Nobina Kallhäll E28 Ers.trafik4027 Kärrtorp C Keolis E19

Lidingö C Keolis Lidingö E22 Terminalgata 12 genomgående

120

4122 Liljeholmen KeolisStockholm sydväst, Södertälje

E19 Ers.trafik Dockning

4323 Lillgården Nobina Södertörn E23Munsö

4331 Märsta stn. Arriva Märsta E16 Ers.trafik4342 Mörby C Arriva Norrort, Norrtälje E20 block B Ers.trafik Lamell4001 Nacka Forum Keolis Nacka/Värmdö E194335 Norrtälje busstn. Nobina Norrtälje E19B 4008 Norsborg Keolis E19

Nykvarn stn. Nobina Södertälje E27 Ers.trafik4041 Nynäshamn Hamn4041 Nynäs stn. Nobina Södertörn E23 Ers.trafik Terminalgata Södra

Odenplan Keolis, Arriva City, Råsta, Norrort, Märsta

E22 Ers.trafik Terminalgata Centrala

Orminge C Keolis Stockholm sydväst

E19 Terminalgata Östra

4328 Oskar Fredriksborg Arriva Norrort1190 Ramsmora terminal Keolis Nacka/Värmdö E194336 Rimbo busstn. Nobina Norrtälje E19B 4034 Rondovägen Nobina, Keolis E234123 Ropsten Keolis City, Lidingö E22 Ers.trafik Ö-terminal Centrala4316 Roslags Näsby trafikplats Arriva Norrort, Norrtälje E20 block B

4138/4304

Rotebro stn/Holmboda Arriva Råsta, Märsta E20 block A Ers.trafik

4033/4035

Rönninge stn. Keolis Stockholm sydväst

E19 Ers.trafik Lamell Södra

Salem C Keolis Stockholm sydväst

E19 Terminalgata Södra

Sickla

4147?/4332 Sigtuna busstn. Arriva Märsta E16

4028 Skarpnäck Keolis E19

4046 Skärholmen KeolisStockholm sydväst, Södertörn

E19 Ers.trafik Lamell Södra

4124 Slussen nedre (Guldfjärdsplan) Keolis Nacka/Värmdö, Södertörn

E19 Ers.trafik Lamell Östra

Slakthusområdet

Slussen övre (Södermalmstorg) Keolis

City, Södertörn, Stockholm sydväst, Nacka/Värmdö,

E22 Ers.trafik, priv. Linjetrafik

Lamell Centrala

4327 Smådalarö Nobina Södertörn E23SockenplanSofia

4305 Sollentuna stn. Arriva, Nobina Råsta, Norrort, Kallhäll

E20a Ers.trafik Ö-terminal Norra

4129 Solna C Arriva, Keolis Råsta, Stockholm sydväst

E20a Ers.trafik, priv. linjetrafik

Ö-terminal Västra

4125 Spånga stn. Arriva Lunda, Råsta E20a Ers.trafik, priv. linjetrafik

Lamell Västra

4044 Spångbro Nobina Södertörn E23 Ö-terminal Södra4014 Stavsnäs vinterhamn terminal Keolis Nacka/Värmdö E19

Stockby4045 Stockholm Syd4137 Stäket

Sundbyberg stn. Arriva Råsta E20 block A Ers.trafik Terminalgata Norra4326 Svartbäcken Nobina Södertörn E23

Söderhall trafikplats Nobina Norrtälje, Märsta E19B Södertälje centrum (Jovisgatan) Nobina Södertälje E27 TerminalgataSödertälje centrum (Köpmangatan) Nobina Södertälje E27 Terminalgata

4038 Södertälje centrum (Södertälje stn.) Nobina Södertälje E27 Ers.trafik TerminalgataSödertälje hamn Nobina Södertälje E27 Ers.trafikSödertälje syd Nobina Södertälje E27 Ers.trafikTappström / Ekerö C Mälaröarna E16

1196 Telefonplan Keolis Stockholm sydväst

E19 Ers.trafik Terminalgata Södra

4020 Trollbäcken C (Alléplan) Nobina Södertörn E234040 Trollsta/Grödby Nobina Södertörn E23

4009/4032

Tullinge stn. Keolis Stockholm sydväst

E19 Ers.trafik

4010 Tumba stn. Keolis Stockholm sydväst

E19 Ers.trafik

4019 Tyresö C (Bollmora C) Nobina Södertörn E23

4315 Täby C Arriva Norrort E20 block B Ers.trafik Dockning utomhus

Norra

Ulriksdals stn. Arriva Råsta E20 block A Ers.trafik Terminalgata4134 Universitetet Keolis City E22

Uppl. Bro stn. Nobina Kallhäll E28 Ers.trafik4321/4569

Uppl. Väsby stn. Arriva Märsta E16 Ers.trafik

Uppsala Centralstation UL

4145 Ursvik4333 Vagnsunda Nobina E19b4341 Vallentuna stn. E20 block B Ers.trafik Terminalgata

Vårsta Keolis Stockholm sydväst

E19 Terminalgata Södra

Vällingby C Arriva, Nobina Lunda, Råsta, Kallhäll

E20A Ers.trafik Lamell Västra

4031 Västerhaninge Nobina Södertörn E23 Ers.trafik Terminalgata Södra4318 Åkersberga stn. Arriva, Nobina Norrort, Roslagen E20a Ers.trafik Ö-terminal Norra

1203 Årstaberg stn. Keolis Stockholm sydväst

E19 Ers.trafik

4337 Älmsta Nobina Norrtälje E19b

4127 Älvsjö stn. Keolis Stockholm sydväst

E19 Ers.trafik

Ösmo centrum Nobina Södertörn E23 Terminalgata Södra4043 Ösmo stn. Nobina Södertörn E23 Ers.trafik Ö-terminal Södra4329 Östersjö Brygga Nobina Norrtälje E19b4037 Östertälje stn. Nobina Södertälje E27 Ers.trafik

4126 Östra stn. / Tekniska högskolan Keolis / Nobina Norrtälje, City, Norrort

E19B, E22 Ers.trafik Ö-terminal och terminalgata

4347 Överby Brygga Keolis Nacka/Värmdö E19

TRITA ABE-MBT-20396

www.kth.se