URBAN TRANSPORT PLANNING 06CV843

URBAN TRANSPORT PLANNING 06CV843

SYLLABUSURBAN TRANSPORT PLANNINGPART - AUNIT - 1INTRODUCTION:Scope of Urban transport planning Inter dependency of land use and traffic System Approach to urban planning6 HoursUNIT - 2STAGES IN URBAN TRANSPORT PLANNING:Trip generation Trip production - Trip distribution Modal split Trip assignment6 HoursUNIT - 3URBAN TRANSPORT SURVEY- Definition of study area-Zoning-Types of Surveys Inventory of transportation facilities Expansion of data from sample8 HoursUNIT - 4TRIP GENERATION:Trip purpose Factors governing trip generation and attraction Category analysis Problems on above5 HoursPART - BUNIT - 5TRIP DISTRIBUTION:Methods Growth factors methods Synthetic methods Fractor and Furness method and problems on the above.5 HoursUNIT - 6MODAL SPLIT:Factors affecting characteristics of split Model split in urban transport planning problems on above6 HoursUNIT - 7TRIP ASSIGNMENT:Assignment Techniques Traffic fore casting Land use transport models Lowry Model Garin Lowry model Applications in India (No problems on the above)8 HoursUNIT - 8URBAN TRANSPORT PLANNING FOR SMALL AND MEDIUM CITIES:Introduction Difficulties in transport planning Recent Case Studies8 Hours

UNIT 14INTRODUCTION41.1 Scope of Urban Transport Planning:41.2 Land Use:4UNIT 27STAGES IN URBAN TRANSPORT PLANNING7UNIT 310URBAN TRANSPORT SURVEY103.1 Study area103.2 Zoning103.3 Types of surveys113.4 Sample expansion13UNIT 414TRIP GENERATION144.1 Trip purpose144.2 Factors affecting trip generation144.3 Types of trip144.4 Category analysis15UNIT 519TRIP DISTIBUTION195.1 Introduction195.2 Growth factor methods19UNIT 622MODAL SPLIT226.1 Introduction226.2 Factors influencing the choice of mode226.3 Types of modal split models236.4 Binary logit model246.5 Multinomial logit model266.6 Problems27UNIT 728TRIP ASSIGNMENT287.1 Introduction287.2 Assignment Techniques287.3 Land Use Models297.4 LOWRY LAND USE MODEL317.5 Lowry-Garin Model337.6 Traffic fore casting35UNIT 839URBAN TRANSPORT PLANNING FOR SMALL AND MEDIUM39CITIES398.1 Introduction398.2 Difficulties in transport planning40

UNIT 1INTRODUCTION1.1 Scope of Urban Transport Planning:Transportation in urban areas relies on the application of technology and scientific principles to plan, design, construct, operate, maintain, and manage in a safe, rapid, comfortable, convenient, economical, and environmentally compatible way the infra-structure and facilities associated with the movement of people and goods. A balanced urban transportation program may better utilize space and energy by improving and upgrading the existing transportation infrastructure and public transport, and by applying traffic control management strategies in order to maintain efficient movement of traffic on existing networks, improve mobility, and reduce traffic congestion, energy use, noise, accidents, pollution, and delays. Demand management measures may include ridesharing programs, staggered work hours, and encouraging the use of carpools, vanpools, and high-occupancy vehicles. Electronic technologies, control software, systems engineering, and integrated applications of advanced surveillance, communications, computer, display, and control process technologies, may be used, both in vehicles and on highways or guideways. Automated guideway transit may include shuttle and loop transit, group rapid transit, and personal rapid transit. This guide furnishes a review of the literature in the collections of the Library of Congress on urban transportation. 1.2 Land Use:

Land use characteristics and transportation are mutually interrelated. The use of the term land use is based on the fact that through development, urban space put up a variety of human activities. Land is a convenient measure of space and land use provides a spatial framework for urban development and activities. The location of activities and their need for interaction creates the demand for transportation, while the provision of transport facilities influences the location itself. Land uses, by virtue of their occupancy, are supposed to generate interaction needs and these needs are directed to specific targets by specific transportation facilities. The following diagram explains the transportation land use interaction.

Land use means spatial distribution or geographical pattern of the city, residential area, industry, commercial areas and the space set for governmental, institution or recreational purposes. Most human activities, economic, social or cultural involve a multitude of functions, such as production, consumption and distribution. These functions are occurring within an activity system where their locations and spatial accumulation form the land uses. So, the behavioral patterns of individuals, institutions and firms will have an impression on the land use. Land use system The essential components of the land use system in terms of land use transport modeling are location and development. The urban land use is largely modeled by simulating the mechanisms that effect the spatial allocation of urban activities in the city. A number of other important economic concepts underpin land use transport models, serving as proxies for the complex interactions and motivations driving urban location. Among these are the ideas of bid rent, travel costs, inertia (stability of occupation of land), topography, climate, planning, and size. Transport system The second major component of a land use transport model, simulated along side land use is the transport system the traditional way of characterizing the transportation system in urban simulation models is a four stage process. The process begins with modeling travel demand and generating an estimate of the amount of trips expected in the urban system .the second phase trip distribution allocates the trips generated in origin zones to destinations in the urban area. The third phase is modal split. Here trips are apportioned to various modes of transport. The four stage simulation processes concludes with trip assignment module that takes estimated trips that have been generated, distributed and sorted by mode and loads it on to various segments of the transport network. Factors affecting transport land use relationship 1. Urban land development 2. Dominance of private vehicle ownership 3. Context of land use and transportation decision making 4. Different time contexts for response.

UNIT 2STAGES IN URBAN TRANSPORT PLANNINGTravel demand modeling aims to establish the spatial distribution of travel explicitly by means of an appropriate system of zones. Modeling of demand thus implies a procedure for predicting what travel decisions people would like to make given the generalized travel cost of each alternatives. The base decisions include the choice of destination, the choice of the mode, and the choice of the route. Although various modeling approaches are adopted, we will discuss only the classical transport model popularly known as four-stage model (FSM).The general form of the four stage model is given in Figure 5:2. The classic model is presented as a sequence of four sub models: trip generation, trip distribution, modal split, trip assignment. The model starts with defining the study area and dividing them into a number of zones and considering the entire transport network in the system. The database also include the current (base year) levels of population, economic activity like employment, shopping space, educational, and leisure facilities of each zone. Then the trip generation model is evolved which uses the above data to estimate the total number of trips generated and attracted by each zone.The next step is the allocation of these trips from each zone to various other destination zones in the study area using trip distribution models. The output of the above model is a trip matrix which denotes the trips from each zone to every other zone. In the succeeding step the trips are allocated to different modes based on the modal attributes using the modal split models. This is essentially slicing the trip matrix for various modes to generate a mode specific trip matrix. Finally, each trip matrix is assigned to the route network of that particular mode using the trip assignment models. The step will give the loading on each link of the network.The classical model would also be viewed as answering a series of questions (decisions) namely how many trips are generated, where they are going, on what mode they are going, and finally which route they are adopting. The current approach is to model these decisions using discrete choice theory, which allows the lower level choices to be made conditional on higher choices. For example, route choice is conditional on the mode choice. This hierarchical choices of trip is shown in Figure 5:3 The highest level to find all the trips Ti originating from a zone is calculated based on the data and aggregate cost term Ci***. Based on the aggregate travel cost Cij** from zone 'i' to the destination zone j, the probability (pj|i) of trips going to zone 'j' is computed and subsequently the trips Tij** from zone 'i' to zone 'j' by all modes and all routes are computed.

Next, the mode choice model compute the probability (pm|ij) of choosing mode 'm' based on the travel cost Cjm* from zone 'i' to zone 'j', by mode 'm' is determined. Similarly, the route choice gives the trips Tijmr from zone i to zone j by mode 'm' through route 'r' can be computed. Finally the travel demand is loaded to the supply model, as stated earlier, will produce a performance level. The purpose of the network is usually measured in travel time which could be converted to travel cost. Although not practiced ideally, one could feed this back into the higher levels to achieve real equilibrium of the supply and demand.In a nutshell, travel demand modeling aims at explaining where the trips come from and where they go, and what modes and which routes are used. It provides a zone wise analysis of the trips followed by distribution of the trips, split the trips model wise based on the choice of the travelers and finally assigns the trips to the network. This process help to understand the effects of future developments in the transport networks on the trips as well as the influence of the choices of the public on the flows in the network.

UNIT 3URBAN TRANSPORT SURVEY3.1 Study areaOnce the nature of the study is identified, the study area can be defined to encompass the area of expected policy impact. The study area need not be confirmed by political boundaries, but bounded by the area influenced by the transportation systems. The boundary of the study area is defined by what is called as external cordon or simply the cordon line. A sample of the zoning of a study area is shown in figure 6:1Interactions with the area outside the cordon are defined via external stations which effectively serve as doorways to trips, into, out of, and through the study area. In short, study area should be defined such that majority of trips have their origin and destination in the study area and should be bigger than the area-of-interest covering the transportation project.

3.2 ZoningOnce the study area is defined, it is then divided into a number of small units called traffic analysis zones (TAZ) or simply zones. The zone with in the study area are called internal zones are in modeling as if all their attributes and properties were concentrated in a single point called the zone centroid. The centroids are connected to the nearest road junction or rail station by centroid connectors. Both centroid and centroid connectors are notional and it is assumed that all people have same travel cost from the centroid to the nearest transport facility which is the average for a zone. The intersection from outside world is normally represented through external zones. The external zones are defined by the catchment area of the major transport links feeding to the study area. Although the list is not complete, few guidelines are given below for selecting zones.1. Zones should match other administrative divisions, particularly census zones.2. Zones should have homogeneous characteristics, especially in land use, population etc.3. Zone boundaries should match cordon and screen lines, but should not match major roads.4. Zones should be as smaller in size as possible so that the error in aggregation caused by the assumption that all activities are concentrated at the zone centroids is minimum.3.3 Types of surveysTypical information required from the data collection can be grouped into four categories, enumerated as below.1. Socio-economic data: Information regarding the socio-economic characteristics of the study area. Important ones include income, vehicle ownership, family size, etc. This information is essential in building trip generation and modal split models.2. Travel surveys: Origin-destination travel survey at households and traffic data from cordon lines and screen lines (defined later). Former data include the number of trips made by each member of the household, the direction of travel, destination, the cost of the travel, etc. The latter include the traffic flow, speed, and travel time measurements. These data will be used primarily for the calibration of the models, especially the trip distribution models.3. Land use inventory: This includes data on the housing density at residential zones, establishments at commercial and industrial zones. This data is especially useful for trip generation models.4. Network data: This includes data on the transport network and existing inventories. Transport network data includes road network, traffic signals, junctions etc. The service inventories include data on public and private transport networks. These particulars are useful for the model calibration, especially for the assignment models.3.3.1 Household dataTo understand the behavior and factors affecting the travel, one has got the origin of travel when the decision for travel is made. It is where people live as family which is the household. Therefore household data is considered to be the most basic and authentic information about the travel pattern of a city. Ideally one should take the details of all the people in the study to get complete travel details. However, this is not feasible due to large requirement of time and resources needed. In addition this will cause difficulties in handling these large data in modeling stage. Therefore, same sample households are randomly selected and survey is conducted to get the household data. Higher sample size is required from large population size, and vice-versa. Normally minimum ten percent samples are required for population less than 50,000. 5 but for apopulation more than one million require only one percent for the same accuracy.3.3.2 Questionnaire designThe next step in the survey is the questionnaire design. A good design will ensure better response from the respondent and will significantly improve the quality of data. Design of questionnaire is more of an art than a science. However few guiding principles can be laid out. The questionnaire should be simple, direct, should take minimum time, ans should cause minimum burden to the respondent. Traditional household survey has three major sections; household characteristics, personal characteristics, and trip details.a) Household characteristics This section includes a set of questions designed to obtain socioeconomic information about the household. Relevant questions are: number of members in the house, no. of employed people, number of unemployed people, age and sex of the members in the house etc., number of two-wheelers in the house, number of cycles, number of cars in the house etc., house ownership and family income.b) Personal characteristics This part includes questions designed to classify the household members (older than 5) according to the following aspects: relation to the head of the household (e.g. wife, son), sex, age, possession of a driving license, educational level, and activity.c) Trip data This part of the survey aims at detecting and characterizing all trips made by the household members identified in the first part. A trip is normally defined as any movement greater than 300 meters from an origin to a destination with a given purpose. Trips are characterized on the basis of variables such as: origin and destination, trip purpose, trip start and ending times, mode used, walking distance, public-transport line and transfer station or bus stop (if applicable).

3.3.3 O-D surveySometime four small studies, or to get a feel of the O-D pattern without doing elaborate survey, work space interviews are conducted to find the origin-destination of employers in a location. Although they are biased in terms of the destination, they are random in terms of the mode of travel.3.3.4 Road side interviewsThese provide trips not registered in a household survey, especially external-internal trips. This involves asking questions to a sample of drivers and passengers of vehicles crossing a particular location. Unlike household survey, the respondent will be asked with few questions like origin, destination, and trip purpose. Other information like age, sex, and income can also be added, but it should be noted that at road-side, drivers will not be willing to spend much time for survey.3.3.5 Cordon and screen-line surveyThese provide useful information about trips from and to external zones. For large study area, internal cordon line can be defined and surveying can be conducted. The objective of the survey is primarily to collect the origin and destination zones and for this many suitable methods can be adopted. It could be either recording the license plate number at all the external cordon points or by post-card method. 5 Screen lines divide the study area into large natural zones, like either sides of a river, with few crossing points between them. The procedure for both cordon-line and screen-line survey are similar to road-side interview. However, these counts are primarily used for calibration and validation of the models.3.4 Sample expansionThe second step in the data preparation is to amplify the survey data in order to represent the total population of the zone. This is done with the help of expansion factor which is defined as the ratio of the total number of household addressed in the population to that of the surveyed. A simple expansion factor Fi for the zone I could be of the following form.

where a is the total number of household in the original population list, b is the total number of addresses selected as the original sample, and d is the number of samples where no response was obtained.UNIT 4TRIP GENERATION4.1 Trip purposeTrip generation is the first stage of the classical first generation aggregate demand models. The trip generation aims at predicting the total number of trips generated and attracted to each zone of the study area. In other words this stage answers the questions to how many trips" originate at each zone, from the data on household and socioeconomic attributes. In this section basic definitions, factors affecting trip generation, and the two main modeling approaches; namely growth factor modeling and regression modeling are discussed.4.2 Factors affecting trip generationThe main factors affecting personal trip production include income, vehicle ownership, house hold structure and family size. In addition factors like value of land, residential density and accessibility are also considered for modeling at zonal levels. The personal trip attraction, on the other hand, is influenced by factors such as roofed space available for industrial, commercial and other services. At the zonal level zonal employment and accessibility are also used. In trip generation modeling in addition to personal trips, freight trips are also of interest. Although the latter comprises about 20 percent of trips, their contribution to the congestion is significant. Freight trips are influenced by number of employees, number of sales and area of commercial firms.4.3 Types of tripSome basic definitions are appropriate before we address the classification of trips in detail. We will attempt to clarify the meaning of journey, home based trip, non home based trip, trip production, trip attraction and trip generation.Journey is an out way movement from a point of origin to a point of destination, where as the word trip" denotes an outward and return journey. If either origin or destination of a trip is the home of the trip maker then such trips are called home based trips and the rest of the trips are called non home based trips. Trip production is defined as all the trips of home based or as the origin of the non home based trips. See figure 7:1 Trips can be classified by trip purpose, trip time of the day, and by person type. Trip generation models are found to be accurate if separate models are used based on trip purpose. The trips can be classified based on the purpose of the journey as trips for work, trips for education, trips for shopping, trips for recreation and other trips. Among these the work and education trips are often referred as mandatory trips and the rest as discretionary trips. All the above trips are normally home based trips and constitute about 80 to 85 percent of trips.

The rest of the trips namely non home based trips, being a small proportion are not normally treated separately. The second way of classification is based on the time of the day when the trips are made. The broad classification is into peak trips and off-peak trips. The third way of classification is based on the type of the individual who makes the trips. This is important since the travel behavior is highly influenced by the socio economic attribute of the traveler and are normally categorized based on the income level, vehicle ownership and house hold size.4.4 Category analysis4.4.1 Growth factor modelingGrowth factor modes tries to predict the number of trips produced or attracted by a house hold or zone as a linear function of explanatory variables. The models have the following basic equation:Ti = fitiwhere Ti is the number of future trips in the zone and ti is the number of current trips in that zone and fi is a growth factor. The growth factor fi depends on the explanatory variable such as population (P) of the zone , average house hold income (I) , average vehicle ownership (V). The simplest form of fi is represented as follows where the subscript " d" denotes the design year and the subscript "c" denotes the current year.Example Given that a zone has 275 household with car and 275 household without car and the average trip generation rates for each groups is respectively 5.0 and 2.5 trips per day. Assuming that in the future, all household will have a car, find the growth factor and future trips from that zone, assuming that the population and income remains constant.

Therefore growth factor models are normally used in the prediction of external trips where no other methods are available.But for internal trips, regression methods are more suitable and will be discussed in the following section.4.4.2 Regression methodsThe general form of a trip generation model isTi = f(x1; x2; x3; ::::xi; :::xk) (7.3)Where xi's are prediction factor or explanatory variable. The most common form of trip generation model is a linear function of the formTi = a0 + a1x1 + a2x2 + :::aixi::: + akxk (7.4)where ai 's are the coefficient of the regression equation and can be obtained by doing regression analysis. The above equations are called multiple linear regression equation, and the solutions are tedious to obtain manually.However for the purpose of illustration, an example with one variable is given.Example Let the trip rate of a zone is explained by the household size done from the field survey. It was found that the household size are 1, 2, 3 and 4. The trip rates of the corresponding household is as shown in the table below. Fit a linear equation relating trip rate and household size.

UNIT 5TRIP DISTIBUTION5.1 IntroductionThe decision to travel for a given purpose is called trip generation. These generated trips from each zone are then distributed to all other zones based on the choice of destination. This is called trip distribution which forms the second stage of travel demand modeling. There are a number of methods to distribute trips among destinations; and two such methods are growth factor model and gravity model. Growth factor model is a method which responds only to relative growth rates at origins and destinations and this is suitable for short term trend extrapolation. In gravity model, we start from assumptions about trip making behavior and the way it is influenced by external factors. An important aspect of the use of gravity models is their calibration that is the task of fixing their parameters so that the base year travel pattern is well represented by the model.5.2 Growth factor methods5.2.1 Uniform growth factor (synthetic method)If the only information available is about a general growth rate for the whole of the study area, then we can only assume that it will apply to each cell in the matrix that is a uniform growth rate. The equation can be written as:Tij = f X tij (8.2)where f is the uniform growth factor tij is the previous total number of trips, Tij is the expanded total number of trips. Advantages are that they are simple to understand, and they are useful for short-term planning.Limitation is that the same growth factor is assumed for all zones as well as attractions.5.2.3 Doubly constrained growth factor model (Fractor and Furness method)When information is available on the growth in the number of trips originating and terminating in each zone, we know that there will be different growth rates for trips in and out of each zone and consequently having two sets of growth factors for each zone. This implies that there are two constraints for that model and such a model is called doubly constrained growth factor model. One of the methods of solving such a model is given by Furness who introduced balancing factors ai and bj as follows:Tij = tij X ai X bj (8.3)In such cases, a set of intermediate correction coefficients are calculated which are then appropriately applied to cell entries in each row or column. After applying these corrections to say each row, totals for each column are calculated and compared with the target values. If the differences are significant, correction coefficients are calculated and applied as necessary. The procedure is given below:1. Set bj = 12. With bj solve for ai to satisfy trip generation constraint.3. With ai solve for bj to satisfy trip attraction constraint.4. Update matrix and check for errors.5. Repeat steps 2 and 3 till convergence.

5.2.4 Advantages and limitations of growth factor modelThe advantages of this method are:1. Simple to understand.2. Preserve observed trip pattern.3. useful in short term-planning.The limitations are:1. Depends heavily on the observed trip pattern.2. It cannot explain unobserved trips.3. Do not consider changes in travel cost.4. Not suitable for policy studies like introduction of a mode.

UNIT 6MODAL SPLIT6.1 IntroductionThe third stage in travel demand modeling is modal split. The trip matrix or O-D matrix obtained from the trip distribution is sliced into number of matrices representing each mode. First the significance and factors affecting mode choice problem will be discussed. Then a brief discussion on the classification of mode choice will be made. Two types of mode choice models will be discussed in detail. ie binary mode choice and multinomial mode choice. The chapter ends with some discussion on future topics in mode choice problem.6.2 Factors influencing the choice of modeThe factors may be listed under three groups:1. Characteristics of the trip maker: The following features are found to be important:(a) Car availability and/or ownership;(b) Possession of a driving license;(c) Household structure (young couple, couple with children, retired people etc.);(d) Income;(e) Decisions made elsewhere, for example the need to use a car at work, take children to school, etc;(f) Residential density.2. Characteristics of the journey: Mode choice is strongly influenced by:(a) The trip purpose; for example, the journey to work is normally easier to undertake by public transport than other journeys because of its regularity and the adjustment possible in the long run;(b) Time of the day when the journey is undertaken.(c) Late trips are more difficult to accommodate by public transport.3. Characteristics of the transport facility: There are two types of factors. One is quantitative and the other is qualitative. Quantitative factors are:(a) Relative travel time: in-vehicle, waiting and walking times by each mode;(b) Relative monetary costs (fares, fuel and direct costs);(c) Availability and cost of parkingQualitative factors which are less easy to measure are:(a) Comfort and convenience(b) Reliability and regularity(c) Protection, securityA good mode choice should include the most important of these factors.6.3 Types of modal split models6.3.1 Trip-end modal split modelsTraditionally, the objective of transportation planning was to forecast the growth in demand for car trips so that investment could be planned to meet the demand. When personal characteristics were thought to be the most important determinants of mode choice, attempts were made to apply modal-split models immediately after trip generation. Such a model is called trip-end modal split model. In this way different characteristics of the person could be preserved and used to estimate modal split. The modal split models of this time related the choice of mode only to features like income, residential density and car ownership.The advantage is that these models could be very accurate in the short run, if public transport is available and there is little congestion. Limitation is that they are insensitive to policy decisions eg: Improving public transport, restricting parking etc. would have no effect on modal split according to these trip-end models.6.3.2 Trip-interchange modal split modelsThis is the post-distribution model; that is modal split is applied after the distribution stage. This has the advantage that it is possible to include the characteristics of the journey and that of the alternative modes available to undertake them. It is also possible to include policy decisions. This is beneficial for long term modeling.6.3.3 Aggregate and disaggregate modelsMode choice could be aggregate if they are based on zonal and inter-zonal information. They can be called disaggregate if they are based on household or individual data.6.4 Binary logit modelBinary logit model is the simplest form of mode choice, where the travel choice between two modes is a made. The traveler will associate some value for the utility of each mode. if the utility of one mode is higher than the other, then that mode is chosen. But in transportation, we have disutility also. The disutility here is the travel cost. This can be represented as

where tvij is the in-vehicle travel time between i and j, twij is the walking time to and from stops, ttij is the waiting time at stops, Fij is the fare charged to travel between i and j, _j is the parking cost, and _ is a parameter representing comfort and convenience. If the travel cost is low, then that mode has more probability of being chosen.Let there be two modes (m=1,2) then the proportion of trips by mode 1 from zone i to zone j is(P 1ij ) Letc1ij be the cost of travelling from zone i to zone j using the mode 1, and c2ij be the cost of travelling from zone I to zone j by mode 2,there are three cases:1. if c2ij - c1ij is positive, then mode 1 is chosen.2. if c2ij - c1ij is negative, then mode 2 is chosen.3. if c2ij - c1ij = 0 , then both modes have equal probability.This relationship is normally expressed by a logit curve as shown in figure 9:1 Therefore the proportion of trips by mode 1 is given by

This functional form is called logit, where cij is called the generalized cost and fi is the parameter for calibration. The graph in figure 9:1 shows the proportion of trips by mode 1 (T 1ij=Tij ) against cost difference.Example Let the number of trips from zone i to zone j is 5000, and two modes are available which has the following characteristics.

Compute the trips made by mode bus, and the fare that is collected from the mode bus. If the fare of the bus is reduced to 6, then find the fare collected.SolutionCost of travel by car (refer to the equation9.1) = ccar = 0:03X20 + 18 X 0:1 + 4 X 0:1 = 2.8Cost of travel by bus (refer to the equation9.1) = cbus =0:03X30+0:04X5+0:06X3+0:1X9 = 2.18

6.5 Multinomial logit modelThe binary model can easily be extended to multiple modes. The equation for such a model can be written as:

Example Let the number of trips from i to j is 5000, and three modes are available which has the following characteristics:

6.6 Problems1. The total number of trips from zone i to zone j is 4200. Currently all trips are made by car. Government has two alternatives- to introduce a train or a bus. The travel characteristics and respective coefficients are given in table. Decide the best alternative in terms of trips carried.

Solution first, use binary logit model to find the trips when there is only car and bus. Then, again use binary logit model to find the trips when there is only car and train. Finally compare both and see which alternative carry maximum trips.Cost of travel by car (refer to the equation9.1) = ccar = 0:05X 25 + 0:02 X 22 + 0:2 X 6 = 6.85

UNIT 7TRIP ASSIGNMENT7.1 IntroductionThe process of allocating given set of trip interchanges to the specified transportation system is usually referred to as traffic assignment. The fundamental aim of the traffic assignment process is to reproduce on the transportation system, the pattern of vehicular movements which would be observed when the travel demand represented by the trip matrix, or matrices, to be assigned is satisfied. The major aims of traffic assignment procedures are:1. To estimate the volume of traffic on the links of the network and obtain aggregate network measures.2. To estimate interzonal travel cost.3. To analyse the travel pattern of each origin to destination (O-D) pair.4. To identify congested links and to collect traffic data useful for the design of future junctions.7.2 Assignment Techniques7.2.1 All-or-nothing assignmentIn this method the trips from any origin zone to any destination zone are loaded onto a single, minimum cost, path between them. This model is unrealistic as only one path between every O-D pair is utilised even if there is another path with the same or nearly same travel cost. Also, traffic on links is assigned without consideration of whether or not there is adequate capacity or heavy congestion; travel time is a fixed input and does not vary depending on the congestion on a link. However, this model may be reasonable in sparse and uncongested networks where there are few alternative routes and they have a large difference in travel cost. This model may also be used to identify the desired path: the path which the drivers would like to travel in the absence of congestion. In fact, this model's most important practical application is that it acts as a building block for other types of assignment techniques. It has a limitation that it ignores the fact that link travel time is a function of link volume and when there is congestion or that multiple paths are used to carry traffic.

7.2.2 User equilibrium assignment (UE)The user equilibrium assignment is based on Wardrop's first principle, which states that no driver can unilaterally reduce his/her travel costs by shifting to another route. User Equilibrium (UE) conditions can be written for a given O-D pair as:

Equation labelqeue2 can have two states.1. If ck = 0, from equation qeue1 fk > 0. This means that all used paths will have same travel time.2. If ck u > 0, then from equation qeue1 f = 0.This means that all unused paths will have travel time greater than the minimum cost path. where fk is the flow on path k, ck is the travel cost on path k, and u is the minimum cost.Assumptions in User Equilibrium Assignment1. The user has perfect knowledge of the path cost.2. Travel time on a given link is a function of the flow on that link only.3. Travel time functions are positive and increasing.7.3 Land Use ModelsThe purpose of land use transport models is to assess the policy impacts in terms of the implications of the future growth patterns on both land use and travel related issues .For this purpose, several researchers have developed various models with different theoretical backgrounds and data requirements. From the early developments of land use transport models to the latest state of art, can be broadly classified into three categories (i)Early models (ii) Intermediate era models (iii) Modern era models.Early modelsThere are several techniques which are representatives of earliest efforts in the development of urban development models and which continue to serve (either in original or modified form) a great number of transportation studies .These techniques are quite simple generally deal with aggregate relationships .These are developed primarily for location of residential activities. In addition many of these techniques can be applied without using computer or simple programs can be prepared for use on a computer .These simple techniques are considered most practical use in smaller urban areas because they require less time, cost and data.a. Activity Weighted Technique b. Density Saturation Gradient Method c. Accessibility Model d. Intervening Opportunities modele. Delphi Technique Intermediate Era Models: This was the golden era of developments in land use transport modeling. Although , a special group of models like empiric model has been developed and applied, the most wide group of models is lead by the work of I. S. Lowry(1964).There are many variants of one or more of these models as applied to particular area.a. Empiric Model b. Lowry Model c. Garin Modeld. Time Oriented Metropolitan Model (TOMM) e. Wilson Model f. Projective Land Use Model g. Hutchinsons Model h. Sarnas Model Modern Era Models: 1980s has seen a very interesting development in the area of land use transport modeling. During the intermediate era, modeling of transport demand and supply has been enhanced with a lot of innovative ideas. The land use / transport modeling also embraced them foe better representation of demand and supply scenario in relation to location. Thus although the basic allocation mechanism emanated from Lowry model was largely used in most models., very complex developments on location process can be found in the models proposed. A significant assimilation of all such developments was taken up by TRL(UK) through a consolidation study reported in 1988.The ISGLUTI (International Study Group On Land-Use/Transport Interaction) study refers to nine models developed originally for different cities of varying sizes and they have been comparatively evaluated for all modal features (Webster, et al, 1988). This has also been tested for geographical transferability. Some of the new land use models like cellular automata are also discussed in the report (Timmermans, 2003). The relationship between land use and transport means that any policy, whether relating specifically to land use development or to the provision of transport facilities, will inevitably affect the other dimension though not necessarily on the same time scale.a. AMERSFOOT b. CALUTAS (Computer Aided Land-Use Transport Analysis System) c. DORTMUND d. ITLUP (Integrated Transportation and Land Use Package) e. LILT (The Leeds Integrated Land-use/Transport Model) f. OSAKA g. SALOC h. TOPAZ i. The MEPLAN Model j. TRANUS 7.4 LOWRY LAND USE MODELThe original Lowry was published in 1964 and since then several important extensions of the original model have been applied to practical planning problems (Hutchinson, 1974). The Lowry model conceives of the major spatial features of an urban area in terms of three broad sectors of activity i.e. basic employment sector, the population serving employment and the household sector. The basic employment is employment whose products and services are utilized outside the study area. With Lowry model, spatial distribution of basic employment is allocated exogenously to the model while the other two activity sectors are calculated by the model by applying an iterative procedure, until the constraints, which are maximum no. of household for each zone and minimum population serving employment for any zone, are satisfied. The flow diagram for this model is shown below.The model views the spatial properties in terms of: 1. Employment in basic industries 2. Employment in population serving industries 3. Household or population sector Basic Employment: - employment in those industries whose products or services depend on markets on external to the region under study. The location of service employment is dependent on the population distribution of the region.

Equation System The above sequence of activities can be expressed in equation as follows.

7.5 Lowry-Garin ModelGarin proposed a formulation of Lowrys model which prevents the need for the iterative solution to the equations described above. Garin has proposed a formulation of the Lowry model, which obviates the need for the iterative solution of to the equations. The following equations can be written:

Successive iterations will yield:

Total employment and total population vectors are given by:

Garin has shown that under certain conditions on the product matrix will converge to the inverse of the matrix ( and the resulting equations will be:

where I=identity matrixGarin argues that if this were not the case then an infinite amount of population serving employment would be generated by a finite number basic employment.7.6 Traffic fore casting

UNIT 8URBAN TRANSPORT PLANNING FOR SMALL AND MEDIUMCITIES8.1 IntroductionUrban travel in Indian cities is dominated by walking, cycling and public transport trips, including those by intermediate public transport (IPT). The variation in modal shares among these three seems to have a relationship between city size and per capita income. Small and medium size cities have a lower income than the mega cities; their dependence on cycle rickshaws and cycles is therefore, more than it is in larger cities.In some cities, though private buses have been introduced recently, the predominant bus transport operation is under the public sector. IPT modes like tempos, autos and cycle rickshaws assume importance as they are necessary to meet travel demands in medium size cities in India like Hubli, Varanasi, Kanpur and Vijayawada. However, there is no policy or projects planned which can improve the operation of para transit modes. Often the fare policy stipulated by the government is not honoured by the operators, nor does the road infrastructure include facilities for these modes. As a result the operators have to violate official rules and policies to survive in the city.Many city governments are either implementing or planning new public transport systems, be it a metro, mono-rail, light rail, sky bus or bus rapid transit systems. The argument given for introducing new technologies is to serve the expected high density demands on a few corridors in the city. In the last decade and a half comprehensive traffic and transport plans have been made for at least fifteen cities. Travel forecasts for the next thirty-forty years have been used to justify the proposals for light rail or metro systems.Indian cities have high density developments in the form of urban slums, while other areas generally do not. However, even a subsidised metro system is much too expensive for the low income population, one reason why the demand for metro systems in Indian cities is so low. Today, the Kolkata, Chennai and Delhi metro systems are carrying less than 20% of their available capacity. The metro and LRT (light rail transit) become low cost in terms of energy consumption and pollution only when the system runs to its full capacity. If the supply exceeds demand, the system runs at a loss8.2 Difficulties in transport planning Cities are locations having a high level of accumulation and concentration of economic activities and are complex spatial structures that are supported by transport systems. The larger the city, the greater its complexity and the potential for disruptions, particularly when this complexity is not effectively managed. The most important transport problems are often related to urban areas and take place when transport systems, for a variety of reasons, cannot satisfy the numerous requirements of urban mobility. Urban productivity is highly dependent on the efficiency of its transport system to move labor, consumers and freight between multiple origins and destinations. Additionally, important transport terminals such as ports, airports, and railyards are located within urban areas, contributing to a specific array of problems. Some problems are ancient, like congestion (which plagued cities such as Rome), while others are new like urban freight distribution or environmental impacts. Among the most notable urban transport problems are: 1. Traffic congestion and parking difficulties: Congestion is one of the most prevalent transport problems in large urban agglomerations, usually above a threshold of about 1 million inhabitants. It is particularly linked with motorization and the diffusion of the automobile, which has increased the demand for transport infrastructures. However, the supply of infrastructures has often not been able to keep up with the growth of mobility. Since vehicles spend the majority of the time parked, motorization has expanded the demand for parking space, which has created space consumption problems particularly in central areas; the spatial imprint of parked vehicles is significant. Congestion and parking are also interrelated since looking for a parking space (called "cruising") creates additional delays and impairs local circulation. In central areas of large cities cruising may account for more than 10% of the local circulation as drivers can spend 20 minutes looking for a parking spot. This practice is often judged more economically effective than using a paying off-street parking facility as the time spent looking for a free (or low cost) parking space as compensated by the monetary savings. Also, many delivery vehicles will simply double-park at the closest possible spot to unload their cargo.2. Longer commuting. On par with congestion people are spending an increasing amount of time commuting between their residence and workplace. An important factor behind this trend is related to residential affordability as housing located further away from central areas (where most of the employment remains) is more affordable. Therefore, commuters are trading time for housing affordability. However, long commuting is linked with several social problems, such as isolation, as well as poorer health (obesity).3. Public transport inadequacy. Many public transit systems, or parts of them, are either over or under used. During peak hours, crowdedness creates discomfort for users as the system copes with a temporary surge in demand. Low ridership makes many services financially unsustainable, particularly in suburban areas. In spite of significant subsidies and cross-financing (e.g. tolls) almost every public transit systems cannot generate sufficient income to cover its operating and capital costs. While in the past deficits were deemed acceptable because of the essential service public transit was providing for urban mobility, its financial burden is increasingly controversial.4. Difficulties for non-motorized transport. These difficulties are either the outcome of intense traffic, where the mobility of pedestrians, bicycles and vehicles is impaired, but also because of a blatant lack of consideration for pedestrians and bicycles in the physical design of infrastructures and facilities.5. Loss of public space. The majority of roads are publicly owned and free of access. Increased traffic has adverse impacts on public activities which once crowded the streets such as markets, agoras, parades and processions, games, and community interactions. These have gradually disappeared to be replaced by automobiles. In many cases, these activities have shifted to shopping malls while in other cases, they have been abandoned altogether. Traffic flows influence the life and interactions of residents and their usage of street space. More traffic impedes social interactions and street activities. People tend to walk and cycle less when traffic is high.6. Environmental impacts and energy consumption. Pollution, including noise, generated by circulation has become a serious impediment to the quality of life and even the health of urban populations. Further, energy consumption by urban transportation has dramatically increased and so the dependency on petroleum. Yet, peak oil considerations are increasingly linked with peak mobility expectations where high energy prices incite a shift towards more efficient and sustainable forms of urban transportation, namely public transit.7. Accidents and safety. Growing traffic in urban areas is linked with a growing number of accidents and fatalities, especially in developing countries. Accidents account for a significant share of recurring delays. As traffic increases, people feel less safe to use the streets.8. Land consumption. The territorial imprint of transportation is significant, particularly for the automobile. Between 30 and 60% of a metropolitan area may be devoted to transportation, an outcome of the over-reliance on some forms of urban transportation. Yet, this land consumption also underlines the strategic importance of transportation in the economic and social welfare of cities.9. Freight distribution. Globalization and the materialization of the economy have resulted in growing quantities of freight moving within cities. As freight traffic commonly shares infrastructures with the circulation of passengers, the mobility of freight in urban areas has become increasingly problematic. City logistics strategies can be established to mitigate the variety of challenges faced by urban freight distribution.

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