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USING MACROSCOPIC FUNDAMENTAL DIAGRAM TO

CHARACTERIZE TRAFFIC FLOW IN URBAN NETWORK

Istiak Ahmed

Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in

partial fulfillment of the requirements for the degree of

Master of Science

In

Civil Engineering

Montasir M. Abbas, Chair

Antonio A. Trani

Alexander Leonessa

December 18, 2015

Blacksburg, VA

Keywords – Macroscopic Fundamental Diagram, Sioux Falls, Traffic Engineering, Emergency

Vehicle Preemption

Copyright© 2015, Istiak Ahmed

USING MACROSCOPIC FUNDAMENTAL DIAGRAM TO

CHARACTERIZE TRAFFIC FLOW IN URBAN NETWORK

Istiak Ahmed

ABSTRACT

Various theories have been proposed to describe vehicular traffic flow in cities on an aggregate level. This dissertation work shows that a number of MFDs exist in an urban network. The number of MFDs basically indicate the existence of different levels of service on different network routes. It also demonstrate that the modification of control strategy can optimize the signal timing plan for the links with high congestion and spillbacks. With the proposed control strategy, the location of points are shifted from lower MFDs to upper MFDs which means the congestion are reduced and the overall network traffic flow operation is improved. In this thesis, the emergency vehicle preemption (EVP) operation is also evaluated by using the MFDs. The concept of MFD can help to illustrate the effect on various types of roads due to EVP operation. The results show that the volume of links along the emergency route is increased and the volume of other links closed to the emergency route is decreased due to preemption. The researchers and practitioners can apply the proposed approach to identify the affected links and minimize the total network delay during EVP.

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ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. Montasir M Abbas, for granting me the opportunity

to work in his research group. I am ever grateful for his guidance and support in my research. His never ending encouragement has helped me to successfully complete my Masters while at Virginia Tech.

Additionally I would like to thank Dr. Antonio A. Trani, for her guidance and valuable

advice in the field of Transportation Engineering, and Dr. Alexander Leonessa, my other committee members, for constructive comments and advice through the entire research process. I also would like to thank my friends Milos Mladenovic, Qichao Wang, Bryan Higgs, Sahar Gainipour and Rubi Han for their valuable guidance in my struggling days.

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DEDICATION

I would like to dedicate this thesis to all those that have supported me in my ambitions. I

would also like to thank everyone for investing their valuable time on me which has made me into the person that I am today. Most of all, I would like to dedicate this work to my parents. They always have supported me in my academic pursuits. Without their encouragement and support I wouldn’t have completed my studies. And finally I would like to dedicate this to all my friends who believed in me even before I did in myself.

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TABLE OF CONTENTS-

ABSTRACT .................................................................................................................................... ii

ACKNOWLEDGEMENTS ........................................................................................................... iii

DEDICATION ............................................................................................................................... iv

TABLE OF CONTENTS- .............................................................................................................. v

LIST OF FIGURES ..................................................................................................................... viii

LIST OF TABLES .......................................................................................................................... x

1 INTRODUCTION ................................................................................................................... 1

1.1 Research Objectives: ........................................................................................................ 2

1.2 Literature Review ............................................................................................................. 2

1.2.1 Macroscopic Fundamental Diagram ......................................................................... 2

1.2.2 Emergency Vehicle Preemption ............................................................................... 5

1.3 Thesis Contribution: ......................................................................................................... 7

1.4 Thesis Organization: ........................................................................................................ 7

2 USING MACROSCOPIC FUNDAMENTAL DIAGRAMS TO CHARACTERIZE THE

PERFORMANCE OF CONTROL STRATEGIES IN URBAN NETWORKS ............................ 8

Abstract ....................................................................................................................................... 8

2.1 Introduction ...................................................................................................................... 9

2.2 Objective .......................................................................................................................... 9

2.3 Literature Review ............................................................................................................. 9

2.4 Methodology .................................................................................................................. 10

2.5 Study Area ...................................................................................................................... 11

2.6 VISSIM Input Data ........................................................................................................ 13

2.6.1 Geometric Data ....................................................................................................... 13

2.6.2 Traffic Control Data ................................................................................................ 13

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2.6.3 Traffic Flow Data .................................................................................................... 13

2.7 Model Calibration Goals ................................................................................................ 14

2.8 VISSIM Model Development ........................................................................................ 14

2.8.1 Base Data Scenario ................................................................................................. 14

2.8.2 Macroscopic Fundamental Diagram for Base Scenario .......................................... 15

2.8.3 Initial Calibration .................................................................................................... 15

2.8.4 Determination of Number of Macroscopic Fundamental Diagram ........................ 18

2.8.5 Evaluation of speed variation on MFD ................................................................... 22

2.9 Changing Control Strategy ............................................................................................. 23

2.10 Modified Traffic Control Parameter .............................................................................. 24

2.11 Results and Analysis ...................................................................................................... 26

2.11.1 Modified macroscopic fundamental diagrams ........................................................ 27

2.12 Discussion ...................................................................................................................... 30

2.13 Conclusion and Future Work ......................................................................................... 33

3 USING MACROSCOPIC FUNDAMENTAL DIAGRAMS TO EVALUATE THE

EMERGENCY VEHICLE PREEMPTION OPERATION IN URBAN NETWORK ................ 35

Abstract ..................................................................................................................................... 35

3.1 Introduction .................................................................................................................... 36

3.2 Objective ........................................................................................................................ 36

3.3 Literature Review ........................................................................................................... 37

3.4 Study Area ...................................................................................................................... 38

3.5 Simulation Input Data .................................................................................................... 39

3.5.1 Geometric Data ....................................................................................................... 39

3.5.2 Traffic Control Data ................................................................................................ 40

3.5.3 Traffic Flow Data .................................................................................................... 40

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3.6 Emergency Vehicle Preemption Strategy ...................................................................... 40

3.6.1 EV Input .................................................................................................................. 40

3.6.2 EV Route ................................................................................................................. 40

3.6.3 Vehicle Class and Types ......................................................................................... 41

3.6.4 Priority and Preemption Activation ........................................................................ 42

3.6.5 Detection Method.................................................................................................... 44

3.7 Goals of Model Calibration ............................................................................................ 45

3.8 VISSIM Model Development ........................................................................................ 45

3.9 Results and Analysis ...................................................................................................... 45

3.9.1 Base Data Scenario ................................................................................................. 46

3.9.2 Initial Calibration .................................................................................................... 47

3.9.3 Determination of Number of Macroscopic Fundamental Diagram ........................ 49

3.9.4 Low and High Volume EV Scenario ...................................................................... 52

3.9.5 Comparison among Scenarios ................................................................................. 53

3.10 Evaluation of EVP Operation ......................................................................................... 56

3.10.1 Change of link location in MFD ............................................................................. 56

3.10.2 Impact on Total Network Performance................................................................... 59

3.11 Conclusion and Future Study ......................................................................................... 61

4 SUMMARY OF FINDINGS AND RECOMMENDATIONS ............................................. 65

4.1 Findings .......................................................................................................................... 65

4.2 Recommendations for Future Research ......................................................................... 66

REFERENCES ............................................................................................................................. 67

VISSIM Input & Output data .............................................................................. 75

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LIST OF FIGURES

Figure 1-1: A well-defined macroscopic fundamental diagram. .................................................... 1

Figure 2-1: Stepwise methodology ............................................................................................... 11

Figure 2-2: Sioux Falls network sources: (a) Google map of Sioux Falls [91]; (b) network

diagram reproduced by Hai Yang and Meng Qiang [56]; and (c) Sioux Falls network in VISSIM

....................................................................................................................................................... 13

Figure 2-3: (a) Sioux Falls network; (b) Volume-density fundamental diagram for base scenario;

(legend: MFD 1: red, MFD 2: green, MFD 3: blue). .................................................................... 15

Figure 2-4: Speed-density fundamental diagram for base scenario: (a) Greenshileds model; (b)

Greenburg model; (c) Underwood model; and (d) Northwester group model (legends: blue to

grey to red). ................................................................................................................................... 17

Figure 2-5: Flow chart for determination of number of MFDs .................................................... 20

Figure 2-6: Flow chart for determination of number of MFDs .................................................... 21

Figure 2-7: Volume-density macroscopic fundamental diagram: (a) first MFD; (b) second MFD;

and (c) third MFD (legends: blue to grey to red). ......................................................................... 22

Figure 2-8: Volume-density macroscopic fundamental diagram: (a) Scenario 1; (b) Scenario 2;

(legends: blue to grey to red) ........................................................................................................ 23

Figure 2-9: Modified macroscopic Fundamental Diagram for WOCL Scenario: (a) volume-

density; (b) first MFD; (c) second MFD; and (d) third MFD (legends: blue to green to red). ..... 28

Figure 2-10: Modified macroscopic Fundamental Diagram WOCL+10sec Scenario: (a) speed-

density; (b) volume-density; (c) first MFD; (d) second MFD; and (e) third MFD (legends: blue to

green to red). ................................................................................................................................. 29

Figure 2-11: Modified macroscopic Fundamental Diagram WOCL-10sec Scenario: (a) speed-

density; (b) volume-density; (c) first MFD; (d) second MFD; and (e) third MFD (legends: blue to

green to red). ................................................................................................................................. 29

Figure 2-12: Macroscopic Fundamental Diagram [3] .................................................................. 30

Figure 3-1: Sioux falls network; sources: (a) google map (b) reproduced by Hai Yang and Meng

Qiang [81] (c) Sioux Falls Network in VISSIM ........................................................................... 39

Figure 3-2: Proposed EV route (path and nodes colored as yellow) ............................................ 41

Figure 3-3: Preemption detectors implementation in one intersection ......................................... 44

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Figure 3-4: (a) Sioux Falls network (b) Volume-density fundamental diagram for base scenario

(legend: MFD 1: red, MFD 2: green, MFD 3: blue) ..................................................................... 47

Figure 3-5: Volume-density fundamental diagram: (a) Greenshileds model; (b) Greenburg

model; (c) Underwood model; and (d) Northwester group model (legends: blue to grey to red). 48

Figure 3-6: Determination of Number of MFDs........................................................................... 51

Figure 3-7: Volume-density macroscopic fundamental diagrams in base scenario: (a) 1st MFD;

(b) 2nd MFD; and (c) 3rd MFD; (legends: blue to grey to red). ..................................................... 52

Figure 3-8: Volume-density fundamental diagram; (a) Low EV; (b) high EV (legends: blue to

green to red). ................................................................................................................................. 53

Figure 3-9: Comparison of first MFDs among three scenarios: (a) base; (b) low EV; and (c) high

EV (legends: blue to grey to red). ................................................................................................. 54

Figure 3-10: Comparison of second MFDs among three scenarios: (a) base; (b) low EV; and (c)

high EV (legends: blue to grey to red). ......................................................................................... 55

Figure 3-11: Comparison of third MFDs among three scenarios: (a) base; (b) low EV; and (c)

high EV (legends: blue to grey to red). ......................................................................................... 56

Figure 3-12: Category of links to describe the effect of EVP on link performance ..................... 58

Figure 3-13: Signal Phase Profile in node 6 ................................................................................. 61

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LIST OF TABLES

Table 2-1: Speed Limit in Sioux Falls Network ........................................................................... 14

Table 2-2: Single Regime Models ................................................................................................ 16

Table 2-3: Calibrated Equation, Value of Parameter and Root Mean Square Error for Each

Model ............................................................................................................................................ 17

Table 2-4: Calibrated Equation and Value of Parameter for Each MFD for base scenario .......... 21

Table 2-5: Comparison between scenarios in evaluation of speed variation ................................ 23

Table 2-6: Signal Control Parameter in Existing and Webster’s method ..................................... 25

Table 2-7: Signal Control Parameter for Congested Intersections for WOCL+10sec and WOCL-

10sec Scenario .............................................................................................................................. 26

Table 2-8: Link number under each MFDs in WOCL, WOCL+10sec and WOCL-10sec scenarios

....................................................................................................................................................... 27

Table 2-9: Change of Link Location among MFDs ...................................................................... 30

Table 2-10: Change of Link Location among MFDs .................................................................... 31

Table 2-11: Total vehicle delay and total stop delay in every scenario ........................................ 32

Table 3-1: Calibrated Equation, Value of Parameter and Root Mean Square Error for Each

Model ............................................................................................................................................ 47

Table 3-2: Calibrated Equation for base scenario ........................................................................ 51

Table 3-3: Signal Timing Table during EVP operation ................................................................ 56



Table 3-6: Total vehicle delay and total stop delay in various scenarios for both routes ............. 60

Table 3-7: Comparison of vehicle delay and stop delay in EV links between scenarios ............. 60

Table A-1: Distance between Initial and Terminal Nodes ............................................................ 75

Table A-2: Peak Hour Factors for O-D Trips ............................................................................... 77

Table A-3: Macroscopic link properties using VISSIM data output for base scenario ................ 79

Table A-4: Macroscopic link properties for WOCL scenario ...................................................... 88

Table A-5: Macroscopic link properties for WOCL+10sec scenario ........................................... 97

Table A-6: Macroscopic link properties for WOCL-10sec scenario .......................................... 106

Table A-7: Macroscopic Link Properties for Low EV scenario ................................................. 114

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Table A-8: Macroscopic Link Properties for High EV scenario ................................................ 123

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1 INTRODUCTION

Urban transportation systems have a hierarchical structure which essentially comprise of freeways and urban roads providing the interrelated infrastructure for mobility and accessibility. The dissimilar traffic flow dynamics of freeway and the urban network challenge the traffic control problem for mixed urban networks. During rush period, integration of the freeways and urban roads will enhance performances compared with separate control policies. An efficient control policy would manage the traffic flow distribution between the urban network and the freeway. The mixed control policy can affect the route choice, thereby the flow distribution, to optimize the whole traffic network [1, 2]. To determine an efficient traffic control strategy, the macroscopic fundamental diagram is a useful modern concept to characterize an urban traffic network. In previous practice, traffic engineers analyze each link of a large network but they don’t have any idea about the traffic flow characteristics of whole network. With individual link characteristic data, it is very difficult to predict the traffic flow of a network. In last 7-8 years, considerable amount of research has focused on the macroscopic

fundamental diagram (MFD). The idea of the Macroscopic Fundamental Diagram (MFD) has developed to establish a relationship between volume and density from empirical and simulation data in homogeneous urban network regions during the last decade [3]. The MFD is basically used to understand traffic flow characteristics in complex urban network. It is also used to evaluate the performance of traffic control strategies in a network. The MFD has been observed for homogeneous urban network regions from empirical and simulation data to improve mobility and to decrease delays in traffic flow, [3]. The shape of the MFD generally depends on the network topology, traffic flow, rate of incoming traffic, peak/off peak period, vehicle route choice, the signal timing plans of the intersections [4], and the infrastructure characteristics [5]. From the previous studies, it is found that homogeneous networks have a well-defined MFD, whereas heterogeneous networks might not have a well-defined MFD [6]. A conceptual macroscopic fundamental diagram is shown in figure 1-1.

Figure 1-1: A well-defined macroscopic fundamental diagram.

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Several studies have tried to figure out the factors of MFD to characterize the traffic flow dynamics in an urban network. With the help of the MFD concept, the relationship of volume-density of large network is used as a network performance diagram to evaluate the traffic flow characteristics of network. Each link belongs to each level of MFDs. In this study, signal control parameters are changed to observe the impact of various control strategies on link characteristics and to characterize the links performance based on their network performance diagram.

After introducing the methodology of evaluating the link characteristics using network

performance diagram, a study is conducted to prove the importance of our new methodology. For this reason, the emergency vehicle preemption (EVP) operation is evaluated by using methodology. The EVP study shows the link performance of a network can be analyzed using the network performance diagram and help to understand traffic flow characteristics of a particular urban network.

1.1 Research Objectives: The major objectives of this research

To characterize the performance of control strategies in urban network by using MFD

To evaluate the performance of EVP using the MFD and to characterize the effect on traffic flow due to EVP in network

1.2 Literature Review

1.2.1 Macroscopic Fundamental Diagram The literature review basically follows examines past efforts with macroscopic fundamental diagram (MFD). It illustrates the general approaches that have been used and focuses on the limitations that will be addressed by this dissertation. Before year 2007, most of the work has been empirical based characterization of traffic flow among various cities. In those studies, the researchers estimate an assumed functional dependence among network-wide traffic variables, such as average speed, road density, average signal spacing, etc. based on the limited empirical data points [7]. The idea of an NFD was originally proposed by Godfrey in 1900 [8] but the empirical observation of its existence in a large-scale urban network is recent [3]. Later Wardrop (1952) and Smeed (1968) developed macroscopic models for arterials. Smeed (1966) gave a hypothesis based on dimensional analysis. In his hypothesis, the maximum flow that can enter the central area of a city should be a function of the area of the city, the fraction devoted to roads and the capacity of the roads, expressed in vehicles per unit time per unit width of road. But this theory does not have any indication about speeds and trip completion rates in an oversaturated period [9-12]. Thomson (1967) found that there seemed to be a linear-decreasing relationship between average speed and flow using data collected from streets in central London for many years. Wardrop (1968) theorized a generic relation between average speed and flow depending on average street width and average intersection spacing. But the relationship was still decreased

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monotonically [10]. Zahavi (1972) proposed that speed was inversely related to flow but still monotonically [13]. Herman and Prigogine (1979) established relationship between average speed and the fraction of moving vehicles in their “two-fluid model” [14]. Herman and Ardekani (1984) further developed and validated the two-fluid model with field data collected in Austin, Houston and other US cities [15]. Later, Herman et al. (1988) found that the two-fluid model is sensitive to driver’s behavior [16]. Williams et al. (1987) and Mahmassani et al. (1987) investigated network level relationships between speed, flow and density using simulation [17]. The empirical observation of its existence in a large-scale urban network is recent [3]. Daganzo & Geroliminis (2007) proposed travel production (the number of vehicle-km traveled per unit time) under steady state conditions can be expressed as a function of the total accumulation of the network [18]. Later, Daganzo & Gayah (2011) reported that MFD of medium city exhibited clockwise loops due to major disturbance causing many drivers to use unfamiliar routes [19, 20]. Ji & Geroliminis (2011) proposed an algorithm to evaluate the spatial compactness of each cluster in the network [21]. Geroliminis & Sun (2011) showed that freeway networks do not have well-defined MFDs between network flow and density [22]. Cassidy, Jang & Daganzo (2011) demonstrated that the stringent single-regime condition necessary to observe a freeway MFD [23]. Daganzo, Gayah & Gonzales (2011) showed the flows of networks consisting multiple overlapping routes are less than flows in homogeneously congested route [19]. Geroliminis & Sun (2011) indicated spatial distribution of vehicle density in the network is one of the key components that affect the scatter of an MFD and its shape [24]. Geroliminis & Boyacı (2012) showed how topology and signal control affect network capacity and the density range [25, 26]. Ji & Geroliminis (2012) indicated partitioning mechanism based on the criteria of a well-defined MFD [2]. Haddad& Geroliminis (2012) proposed a new algorithm to derive the boundaries of the stable and unstable regions [27]. Zheng, Waraich, Axhausen & Geroliminis (2012) combined a macroscopic model of traffic congestion in urban networks with an agent-based simulator to study congestion pricing schemes [6]. Haddad, Ramezani & Geroliminis (2012) formulated urban and freeway flow dynamics with the tool of MFD and asymmetric cell transmission models [28]. . Haddad, Ramezani & Geroliminis (2013) represented a unimodal and low-scatter relationship between region density and outflow on a large-scale mixed transportation network consisting of a freeway and an urban network [29]. Shoufeng, Jie, Zuylen & Ximin (2013) indicated time step for data processing is a determinant for the MFD and GMFD shapes [30]. Srivastava & Geroliminis (2013) showed that the level of the capacity drop depends on the ratio of mainline vs. ramp flow [31]. Leclercq & Geroliminis (2013) represented the flow distribution among routes smoothly varying with the total flow either in free-flow or congestion situations shows rougher for system optimum with discontinuities and far from equity [32]. Aboudolas & Geroliminis (2013) integrated an MFD modeling to perimeter and boundary control optimization for large-scale networks with multiple centers of congestion [33]. Zheng, Aboudolas & Geroliminis (2013) develop a three-dimensional MFD (3D-MFD) relating the accumulation of cars and buses, and the total circulating flow in the network based on simulated data [34]. Zheng & Geroliminis (2013) developed multimodal MFD with optimizing the total passenger hours traveled (PHT) to serve the total demand by redistributing road space among modes [35].

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Hajiahmadi, Haddad, Schutter & Geroliminis (2013) formulated the optimal hybrid control problem for an region MFD network as a mixed integer nonlinear optimization problem considering perimeter controllers and switching signal timing plans controllers [36]. Geroliminis, Haddad & Ramezani (2013) formulated the optimal perimeter control for two-region urban cities with the use of MFDs which is solved by model predictive control, where the prediction model and the plant (reality) are formulated by MFDs [37]. Aboudolas & Geroliminis (2013) employed an optimal control methodology for the design of perimeter and boundary flow control strategies aiming at distributing the accumulation homogeneously and maintaining the rate of vehicles around a desired point [38]. Ekbatani, Yildirimoglu, Geroliminis & Papageorgiou (2013) introduced and tested a new feedback-based gating strategy using MFD or NFD [39]. Zhou, Yi (2013) introduced method for analytical approximation models for the MFDs with equal road length on the corridor which reduces the complexity of the method. He also showed turnings and endogenous traffic can have various impact on the shape of the MFD, the capacity, the critical density, the variance in density and also cause a phenomenon of clustering [40]. Geroliminis, Zheng & Ampountolas (2014) develop a three-dimensional vehicle MFD (3D-vMFD) relating the accumulation of cars and buses, and the total circulating vehicle flow in the network [41]. Hajiahmadi, Haddad, Schutter & Geroliminis (2014) formulated optimal hybrid control problem for a R-region MFD network as a mixed-integer nonlinear optimization problem introducing two types of controllers – perimeter controllers and switching signal timing plans controllers [42]. Aboudolas, Zheng & Geroliminis (2014) develop a three-dimensional vehicle MFD (3D-vMFD) relating the accumulation of cars and buses, and the total circulating vehicle flow in the network [43]. Among the literature of the concept of MFD, it can be said that there are very few studies found about the impact of control strategies on MFD. As it is known that the capacity of a lane or group of lanes is the result of multiplication between the saturation flow rate of that road and the ratio of effective green to cycle length of the signal system in that roadway (see equation 1.1)

C

gsc (1.1)

Where: c = capacity (the maximum hourly volume that can pass through an intersection from a lane or group of lanes under prevailing roadway, traffic, and control conditions), in vehicle/hour s = saturation flow rate in vehicle/hour g/C = ratio of effective green time (seconds) to cycle length (seconds). The characteristics of saturation flow of a particular lane or group of lanes doesn’t vary with other traffic stream parameters expect saturation headway. So, the ratio of green time to cycle length has a significant impact to increase or decrease the capacity of a lane or group of lanes. In this dissertation, we used this concept to characterize the network links using control strategies. From the literature, it is observed that the volume-density fundamental diagram is very scatter in nature for heterogeneous network. So, it is very difficult to represent the volume-density

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relationship using one best-fit MFD curve in traditional way [3, 18]. In this study, multiple MFDs based on their peak level will be introduced to represent the volume-density relationship for a heterogeneous network instead of just one MFD curve. So, MFD curve having higher peak represents groups of lanes or links with higher capacity. The number of MFDs will be selected based on scatter characteristics of volume-density fundamental diagram for a specific dataset in such a way so that each MFD will be the best reprehensive curve of surrounding points. The selection of scattered points for each MFD depend on which calibration formula being used. The above concept of constructing MFD is used to characterize the performance of control strategies in urban network.

1.2.2 Emergency Vehicle Preemption Preemption (PE) is defined by MUTCD [44] as “transfer of normal operation of a traffic control signal to a special control mode of operation”. The main purpose of PE is to alter the regular signal timing plan to accommodate approach and passage of certain classes of approaching vehicles. PE is often provided for trains, boats, emergency vehicles (EV), law enforcement vehicles, and light rail transit [44, 45]. The order of priority among special purpose vehicles is: train, boat, heavy vehicle (fire vehicle, emergency medical service), light vehicle (law enforcement), light rail transit, and rubber-tired transit [44]. There are several types of detection technologies for emergency vehicles that have been used in traffic signal preemption operation [46]. They differ based on their functionality. Detection technologies are divided into three main categories based on their area of detection which are zone, point and continuous detection [16]. Point detection is generally performed using AVI tags and readers or a device that transmits vehicle data using the detector loops [47]. The controller only receives information that vehicle was at certain fixed point at certain time in this detection system [47, 48]. Point based detection depends upon appropriately placed and functional loop detectors [46]. In some of the previous research, detectors were located at a travel time of 180 to 270 seconds (i.e., two or three cycle lengths) upstream [49]. On the contrary, installation in Illinois placed advance loop detectors approximately 250 feet upstream [50]. Zone detection operates by sending the signal when the vehicle is within a certain zone [47, 51]. Zone priority vehicle detection systems includes high intensity light beacons, radio frequency (RF) beacons, and infrared beacons [45]. In optical-based system, the emitter is flashing light on and off at a high frequency coded to identify emergency vehicles [52]. In this system, the emitter is flashing light on and off at a high frequency coded to identify emergency vehicles [52]. Continuous detection relies upon Global Position System (GPS) information [47] in which vehicle is continuously detected at discrete points on the network.

The parameters for preemption and priority strategy are usually set to minimize the intersection delay [53]. The maximum allowable green extension and the minimum green period for the cross traffic are considered as preemption strategy parameter [53]. Sometimes a queue can be prioritized if queue length is greater than specified value [54, 55]. Gang-Len et al. introduced a performance index based preemption decision model [56]. In addition, various types of delay such as vehicle delay, bus schedule delay and passenger delay are considered as performance index [55-

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57]. Moreover, operating speed, timetable adherence and spillback possibility are used as performance indicators for preemption services [58]. In the case of potential queue spillback of the off-ramp, congestion level in the upstream is estimated [59]. A terminal cost is also considered as performance measures which estimates the delays incurred beyond the end of the decision horizon by all the vehicles left in queue at that time under the assumption that no other vehicles join the queue beyond the end of the decision horizon and that queue discharge occurs at a maximum rate [55]. The travel cost of each link is quantified with the volume and presence data collected from the loop detectors that is used as an index that quantifies level of congestion at each link [60]. Performance index weights is also developed dynamically to reflect the changing necessity of TSP under different conditions [61].

Adaptive control is integrated with preemption or priority strategy in some recent studies [57, 58]. An adaptive TSP model uses information of predicted bus arrival information, estimated queue condition, signal status, and pedestrian presence to optimize TSP strategies [62]. Link inflow and outflow are controlled dynamically through an adaptive signal timing scheme [63]. The location of bus stops and duration of loading of bus is considered in some papers [54, 58]. Important information for representing vehicle–transit stop interaction are the type of vehicle, status of the transit stop (occupied or not occupied), and type of transit stop such as bus bay, nearside stop with one and multiple lane [58]. The randomness of a bus arrival time to the stop bar is estimated by considering the bus stop dwell time, the current signal timing and the delay caused by standing vehicle queues [64]. For highly congested flow, the adaptive control queue length was found to be less than the actuated control queue length [57]. To reflect differences in person occupancy between vehicle and transit, higher weighting coefficients are assigned to the transit than passenger car [55, 65]. The bus clearance time at the intersection is very important factor in transition out period but it is assumed negligible sometimes for simplicity [66].

In emergency vehicle preemption studies, Dijkstra's algorithm is used to estimate the shortest route for emergency vehicle [60, 67, 68]. The dynamic preemption module sequentially activates the preemption procedure for the intersections on the selected route depending on the direction and location of an emergency vehicle [60]. Beside minimum and maximum phase interval, some factors such as pedestrian service, alternative minimum times, and a priority service extension are also used to formulate the decision model for priority [69]. For multiple priority requests, a first-come-first-serve rule is used [70]. A multilayer fuzzy model can be used to determine the degree-of-priority based on two emergency vehicle preemption factor - demand intensity and preemption influence intensity [68]. For multi-modal traffic signal control, a mathematical optimization model is used with multiple priority requests based on a hierarchical control policy [71]. For person-capacity-based approach, the bus ratio, bus occupancy, and maximum degree of saturation of exclusive bus lanes are analyzed to measure the performance [72]. A stochastic mixed-integer nonlinear model (SMINP) is used in a real-time transit signal priority control system [73]. The impacts of the priority operation to other traffic should be considered for next priority operation [73]. For evacuation situation, a travel time estimation model for emergency traffic is formulated with a conditional traffic signals priority control method at each intersection [68, 74].

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Four signal timing parameters are considered in the optimization algorithm (cycle length, split, offset and phase sequence) [53]. In addition, non-green period on the main street [53] is considered as cycle parameter in one paper. In some of the previous research, real-time traffic responsive control is used for signal optimization [54, 55, 57, 75]. The signal plan is generated for a projection period of 30-90 sec, depending on the advance traffic information from vehicle detectors [54]. Genetic algorithm is used in signal timing optimization for delay minimization [58, 76, 77]. Heuristic decision-making process is used to generate phase plans to find the best one [55]. The optimal amount of time needed to activate the preemption sequence depends on several factors including the speed of an emergency vehicle, the status of the signal phase and traffic condition at intersection [60]. The VFC-OPAC algorithm determines phase splits by optimizing a performance index [76]. VISGAOST optimizes traffic signal timings for both transit and private modes, and can optimize TSP parameters such as group max, group walk, and max extend values [78].

Among the above literature review, it can be seen that the researchers studied detection technology, EVP strategies, signal timing optimization for EVP operation, route choice, etc. But within my knowledge no one introduce the impact of EVP operation in urban network using the concept of MFD which is quite important for characterization of network traffic flow dynamics. In this thesis, the evaluation of EVP operation in urban network is performed MFD.

1.3 Thesis Contribution: The macroscopic fundamental diagram (MFD) offers a new angle to characterize a network and thus presents a big opportunity for improving the current state-of-practice. This thesis aims to characterize the performance of control strategies in urban network by using MFD. From this study, it can be observed that the uniformity of a MFD doesn’t depend on the homogeneity of a network only. The uniformity can be manipulated by changing cycle length, split and offset. This will be very useful for traffic engineers to optimize the traffic flow in both homogeneous and heterogeneous network. In addition, the emergency vehicle preemption operation is also evaluated with the help of MFD in urban network. From this study, the traffic engineers can easily identify how much the roads and arterials will be affected based on their types during EVP operation.

1.4 Thesis Organization: The thesis is organized into four chapters. Chapter 1 presents an introduction, literature review, research objectives, and contribution of the thesis. Chapter 2 presents a study conducted for the characterization of the performance of traffic control strategies by using the MFDs. Chapter 3 describes the evaluation of the EVP operation using MFDs in urban network. Chapter 4 presents the study conclusions and recommendations for further research. The origin-destination data table and other simulation data used in this study are attached in the Appendixes.

8

2 USING MACROSCOPIC FUNDAMENTAL DIAGRAMS TO CHARACTERIZE THE PERFORMANCE OF CONTROL STRATEGIES IN URBAN NETWORKS

Abstract A macroscopic fund0amental diagram (MFD) is a unimodal and low-scatter relationship between network density and flow. The diagram is useful to analyze the scatter characteristics and spillback in a large network and can be used to characterize and evaluate network performance. In this study, we posit that a number of MFDs exist in a given network, indicating the existence of different levels of service on different network routes. A control strategy is considered to compare its effect on each MFD against the base scenario. The proposed control strategy is to optimize the signal timing plan for the links with high congestion and spillbacks. The proposed strategy is applied to develop more uniform MFDs with less scatter characteristics. The results shows that the volume of congested links is increased and the location of points are shifted from lower MFDs to upper MFDs. This means the congestion of the links are reduced and the overall network traffic flow operation is improved.

9

2.1 Introduction Traffic management systems play a vital role in modern traffic engineering operation and management. One of the important roles of traffic management systems is to monitor and minimize delay and incidents in large-scale urban networks. Creating efficient traffic management systems is challenging to both traffic researchers and practitioners. Urban networks consist of urban road links and signalized intersections, and modeling traffic flow operation of individual elements in a network can be an arduous task [1].

During the last decade, the concept of the Network Fundamental Diagram (NFD), also referred to as the Macroscopic Fundamental Diagram (MFD) has emerged to establish a relationship between volume and density from empirical and simulation data in homogeneous urban network regions [3]. The shape of the MFD was found to depend on the network topology, traffic flow, rate of incoming traffic, peak/off peak period, vehicle route choice, the signal timing plans of the intersections [4], and the infrastructure characteristics [5]. Homogeneous networks were found to have a well-defined MFD, whereas heterogeneous networks might not have a well-defined MFD [6].

To have a better understanding of traffic flow in a large network, the origin-destination (O-

D) trip distribution plays an important role. A balanced O-D trip distribution is a vital factor when minimizing total network delay [79]. The route of the O-D trips are mostly assigned in the network based on some important factors, including shortest path, travel time, and traffic flow conditions of the major roads [80]. O-D data provide information about the most used routes by drivers in the network. This will help to control traffic flow as well as to minimize congestion and spillbacks.

2.2 Objective The objective of this paper is to test the hypothesis that there exist several MFDs on a network, and that each MFD defines a different class of operation on the network. In essence, an engineer might deliberately design control strategies on the network to accommodate and provide a higher class of service for some particular routes. We therefore aim to cluster the volume-density plot into few different MFDs, and identify the links that fall into each MFD. Since the capacity on arterial links can be changed by reallocating the green times in the cycle, we aim to illustrate the concept by re-allocating the green times to move some links from one class of performance to another.

2.3 Literature Review The Fundamental Diagram (FD) is one of the most broadly used theories in the field of traffic flow. It was initially observed to provide a steady-state relationship between traffic variables (speed, density, and flow) for a stretch of highway [81]. The FD can provide a rough sketch of the formation and dissolution of shockwaves. But the FD is not enough to depict complex traffic patterns such as stop-and-go waves and capacity drop phenomena [82, 83]. In a large-scale network, the relationships between flow and density in all links is known as the MFD. Under homogeneity condition, MFD is defined as a low scatter unimodal relationship

10

between network flow and network accumulation. The idea of an MFD was originally proposed by Godfrey in 1900 [8], but the empirical observation of its existence in a large-scale urban network is recent [3]. This macroscopic property is significant for establishing sophisticated perimeter traffic signal control strategies. Researchers claimed that the evaluation of traffic congestion, effectiveness of traffic management systems, causes of accidents, possibility of bottlenecks, and effect of emergency vehicle preemption can then be conducted for the whole network, and not necessarily at each individual link [3, 18].

The MFD is not usually applicable to all types of networks and demand profiles. Recent works identified that the spatial distribution of vehicle density in the network affects the scatter and maximum flow of an MFD [4, 84]. It is also observed that the average network flow is consistently higher with low link density variance for the same network density. On the other hand, higher densities with heterogeneous distribution can create points below an MFD [85]. To solve this problem, past research partitioned heterogeneous networks into a small number of homogeneous regions with similar link density[6].

Researchers using empirical data in Yokohama, Japan [3], found that a well-defined

diagram can exist between space-mean flow and density by aggregating the highly scattered plots of flow versus density from individual loop detectors. In this study, traffic congestion was homogeneously distributed. A MFD was created that reflected the relationship between the number of vehicles and space-mean speed or flow. Some other empirical and simulation studies for MFDs can be found in [19, 84, 86, 87]. In addition, the presence of a heterogeneous network is also common, where various level of congestion and their MFDs are not well established in comparison to a homogeneous network [22, 82, 88].

2.4 Methodology The paper is representing the hypothesis that there exist several MFDs on a network, and that each MFD defines a different class of operation on the network. First of all, a suitable study area was selected. After that a stepwise methodology is prepared to achieve our goal (see figure 2-1). The methodology are discussed below:

11

Figure 2-1: Stepwise methodology

2.5 Study Area The study area is the aggregated network of the city of Sioux Falls (the largest city in South Dakota, US) popularly cited in literature [89]. The Sioux Falls network is composed of 24 nodes and 76 links [90]. The Sioux Falls network is not considered realistic, and a comparison with the actual map explains why [90] (see Figure 2-2(a) and 2-2(b)).

Prepare network properties for

simulation

Run simulation with base data and collection link properties –

volume and denstiy

Comparing four single regime models to calibrate best model

to represent our base data

To determine suitable number of MFDs, comparing RMSE value of different scenario

Analyze and compare the link characteristics of each MFDs

Modify the control system using Webster’s method

12

(a)

(b)

(c)

13

Figure 2-2: Sioux Falls network sources: (a) Google map of Sioux Falls [91]; (b) network diagram reproduced by Hai Yang and Meng Qiang [56]; and (c) Sioux Falls network in VISSIM

2.6 VISSIM Input Data VISSIM 7.0 was the microscopic, time-step and behavior-based traffic simulation model used in this study. Various traffic operations under constraints such as lane configuration, traffic composition, speed limits, traffic signals, and time of day can be analyzed by using VISSIM, is making it a useful tool for evaluating different alternatives.

The VISSIM model setup required the input of geometric, traffic control, and traffic flow data for the study area (Figure 2-2(c)). Highlights from the data collection and field observations relevant to the VISSIM model development are discussed below.

2.6.1 Geometric Data Features that were included in VISSIM 7.0 are: the number of lanes, lane additions, lane drops, auxiliary lanes, highway curvature, and intersection geometry. Geometric information for the Sioux Falls network study was obtained from scaled aerial photographs in bitmap format downloaded from Google Maps [91] and from field observations [90]. The lane configurations and other details of geometric data were taken from aerial photographs and were confirmed based on field observations [90] (given in table A.1 in Appendix A). The eastbound, westbound, southbound and northbound links are numbered as 1 to 16, 17 to 32, 33 to 54, and 55 to 76, respectively (Figure 2-2(b)).

2.6.2 Traffic Control Data Traffic signal timing sheets for the signalized intersections were obtained from the Traffic Engineering Department of the city of Sioux Falls [92] and the signal timing information was fed into VISSIM. Additionally, the location of intersection control was identified using Google aerial map [91] and confirmed using the Sioux Falls signalized intersection network map provided by the Traffic Engineering Department of the city of Sioux Falls [92]. The detail signal timing plan for each intersection includes the ring barrier diagram, cycle length, splits, offset, coordinated phases, and traffic patterns of each intersection.

2.6.3 Traffic Flow Data Origin destination (O-D) trip data are used as traffic flow data relevant to the micro-simulation model’s development. The O-D trip matrices are obtained from the dynamic assignment study by Larry J. LeBlanc et al. [93-95]. The study period is a peak hour with 528 O-D pairs (given in table A.2 in Appendix A). The traffic flows in the network are collected from traffic count book taken by Sioux Falls Public Works Department-Engineering Division and South Dakota Department of Transportation [92, 96, 97]. The vehicle routes are determined according to the O-D data. The default distribution of vehicle types given in VISSIM was used. The speed limit of Sioux Falls network are given in table 1 [98-101]. : The speed limit of each link is determined by observing google maps [102].

14

Table 2-1: Speed Limit in Sioux Falls Network

Road Type Speed Limit (mph)

Urban Interstate 75 Major Urban roads 45-55

Minor Urban and residential roads 35

Ramp 40

2.7 Model Calibration Goals The objective of model calibration in this study was to obtain the best match possible between model performance estimates and existing single regime macroscopic diagram models. It may be noted that there are no universally accepted procedures for conducting calibration and validation for complex transportation networks [8]. The responsibility lies with the modeler to implement a suitable procedure which provides an acceptable level of confidence in the model results. There are four renowned single regime models considered for calibration: Greenshields, Greenburg, Underwood, and Northwestern Group [40, 103-110].

2.8 VISSIM Model Development The Sioux Falls roadway network was originally traced over a scaled aerial photograph imported into VISSIM. The number of lanes, location of lane additions and drops, the frontage road intersections and other roadway geometry were confirmed by Google Maps [91] and Transportation Network Test Problems [90, 92]. Other additional detail was incorporated into the VISSIM network (posted speed limits, traffic signal timing, etc.) to better reflect field conditions.

It was found that not all default VISSIM input parameters represented study area conditions, and some needed to be adjusted to replicate reality. In this study, the equation for the speed-density relationship model have to be calibrated.

2.8.1 Base Data Scenario After implementing all geometric data, traffic control data, and O-D trip data for each intersection, the VISSIM model was run to simulate one peak hour period without warm up period. In this study, the total simulation period is 4500 seconds. First 900 seconds are assumed as warm up period because it is observed that time required to travel longest origin-destination distance is around 700-800 seconds. All macroscopic link properties are collected for the range of 900 to 4500 seconds. The macroscopic link properties for each link such as volume, speed, density, and delay from VISSIM were collected. The vehicle delay and stop delay were also calculated for each link using vehicle travel time measurements. The output data were exported into Microsoft Excel from VISSIM (given in table A.3 Appendix A). The database has over thirty thousand rows, which is quite huge. Among them, around twenty six thousand data are considered as a representative of all link properties. Assuming the vehicle length with headway as 20 ft., maximum lane density is found 264 vehicle/mile/lane. So, the data with density greater than 264 vehicle/mile/lane were omitted.

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2.8.2 Macroscopic Fundamental Diagram for Base Scenario Then, the volume-density macroscopic fundamental diagrams were observed and analyzed for base scenario (Figure 2-3(b)).

(a)

(b)

Figure 2-3: (a) Sioux Falls network; (b) Volume-density fundamental diagram for base scenario; (legend: MFD 1: red, MFD 2: green, MFD 3: blue).

By observing the Figures 2-3(b), it can be noticed that most of the points lie between 0 to 800 vehicles per hour in volume and 0 to 250 vehicles per mile in density. But some of the points, like links 7, are placed on the upper level of the graph, because the volumes of the links are high, possibly indicating major road with higher traffic. The location of link points will be discussed further in following sections.

2.8.3 Initial Calibration To determine the best fit model, four different models were compared: Greenshileds, Greenburg, Underwood and Northwester Group, by using VISSIM macroscopic output data. Table 2 shows the model name, its volume-density relationship equation, and its parameters [40, 103-110]:

Legends:

ascending order

16

Table 2-2: Single Regime Models

Model Name Volume-Density Relationship

Equation Parameters

Greenshields

j

fk

kkuq

2

(2.1) uf, kj

Greenburg

k

kkuq jln0 (2.2) u0, kj

Underwood

0k

k

f keuq (2.3) uf, k0

Northwestern 2

02

1

k

k

f keuq (2.4) uf, k0

Where: u = Speed (miles/hour) k = Density (vehicles/mile/lane) u0 = Optimal speed (miles/hour) uf = Free flow speed (miles/hour) k0 = Optimal density (vehicles/mile/lane) kj = Jam density (vehicles/mile/lane)

The statistical software JMP was used to model each relationship using the VISSIM macroscopic output data (Table 2-2). The speed-density relationships in each model is shown in Figure 3.

(a)

(b)

Legends: ascending order

17

(c) (d)

Figure 2-4: Speed-density fundamental diagram for base scenario: (a) Greenshileds model; (b) Greenburg model; (c) Underwood model; and (d) Northwester group model (legends: blue to grey to red).

Table 2-3: Calibrated Equation, Value of Parameter and Root Mean Square Error for Each Model

Model Name Calibrated Equation

Optimal speed,

u0 (mph)

Free flow

speed, uf

(mph)

Optimal density, k0 (veh/ hr/lane)

Jam density, kj (veh/hr/lane)

Root Mean

Square Error

(RMSE)

Greenshields

47.23297.10

2kkq (2.5) 10.97 223.47 189.72

Greenburg

kkq

15.215ln49.8 (2.6) 8.49 215.15 129.68

Underwood

50.3226.55

k

keq (2.7) 55.26 32.50 83.30

Northwestern 2

23.252

1

38.46

k

keq (2.8) 46.38 25.23 99.16

By comparing the figures of each model (Figure 2-4), it can be observed that the figure for

the Greenburg and Greenshields models is very poorly fitted. But the diagrams for the Underwood, and Northwestern models are well fitted based on data.

After fitting this four models to our data set, they can be compared by error measures in

the estimation period. Root mean squared error (RMSE) takes precedence over the others [111]. In literature, the RMSE is a frequently used measure of the differences between values predicted by a model or an estimator and the values actually observed. RMSE is a good measure of accuracy but only to compare forecasting errors of different models for a particular variable and not between variables [112-116]. The root mean squared error can only be compared between models whose

18

errors are measured in the same units [111]. RMSE gives the standard deviation of the model prediction error. There is no standard values or any kind of standard range of RMSE [111, 117, 118]. A smaller value indicates better model performance [111, 117, 119]. In the context of online operational application of DTA simulation modeling, Mahmassani H.S. et al used a transfer function model to represent speed-density relationship. The parameters in the transfer function model was calibrated using proposed optimization approach [113, 120]. They used least squared error criteria to calibrate the best estimates of the parameters. In our study we use root mean squared error criteria to calibrate the parameters.

For the comparison of accuracy between models over our dataset, RMSE is used as a

performance measure. Table 2-3 presents the calibration results and associated goodness-of-fit statistics—root-mean-square error (RMSE) of volume. To compare with the performance of the single regime traffic flow models, similar results are shown in the table for Greenshields, Underwood, and Northwestern models shows that RMSE is the least for the Underwood model. So, for this volume-density relationship, the Underwood model is the best calibrated model in compare with other three. So, the calibrated equation for volume-density relationship is:

0k

k

f keuq (2.9)

Where, free flow speed, uf = 55.26 mile/hour and optimal density, ko = 32.50 vehicle/ hour/lane. Now, the authors have to develop the MFDs based on the calibrated equation and simulation data.

2.8.4 Determination of Number of Macroscopic Fundamental Diagram The network is heterogeneous type which means the characteristics of links, number of lane per link, link density, vehicle input are different from each other. All the input data are collected from field survey to develop a realistic heterogeneous network. By observing the volume-density fundamental diagram, it can be seen that the link points are very scatter with increase of higher density. So, it is very difficult to represent the relationship of a heterogeneous network using one MFD curve in traditional way [3, 18]. Instead of only one MFD, multiple MFDs will be introduced by differing their peak level. So, MFD with higher peak represents link with higher capacity. The number of MFDs will be determined based on scatter characteristics of volume-density fundamental diagram for a specific dataset in such a way so that each MFD will be the best reprehensive curve of surrounding link points. The link points selected for each MFD are also dependent on which calibration formula being used. Here, different number of MFDs were introduced to determine how many MFDs were perfect to represent our macroscopic link data. For performance measurement, root mean square error were considered to compare among the cases of different MFDs (figure 2-6). The following steps are used for this process:

19

1. As the output dataset was huge and difficult to use in excel solver, we choose some representative points from each of the links in such a way so that the chosen points of one link are representing the whole scatter characteristics of that link. 2. Input data were link number, density and observed volume from simulation results. As Underwood model is the best calibrated model for this dataset, the estimated volume corresponding to each density were calculated by this model. 3. Assume a link parameter for each link (L1, L2,….., L76) to determine which link is belong to which MFDs. 4. Consider the output macroscopic link data are representing by two MFDs. 1st MFD and 2nd MFD were representing as 1 and 2. 5. Assuming one output density data of link 1 is D1. To determine the estimated volume by Underwood model for each density data, use “If L1 = 1, then, D1 belongs to 1st MFD; otherwise for L1 = 2, then, D1 belongs to 2nd MFD. This way, all the estimated volume were calculated. Initially, All L values were assumed as1. 5. Determine total root mean square error (RMSE) by summing RMSE for each MFDs. 6. Now Excel Solver was used to minimize the RMSE along with which link belongs to which MFDs. The model objective function, variables and constraints were given below: Objective Function: Minimize RMSE = SQRT [Σ(square errors between observed volume and estimated volume)/(Number of data points)] Variables: uf 1, uf 2 = free flow speed for each MFD, ko1, ko1 = optimal density for each MFD L1, L2,…., L76 = link parameters for each link Constraints:

2,....,,1 7621 LLL

75,0 21 ff uu

264,0 21 oo kk

7. Three other special constraints were also considered assuming 1st MFD with higher capacity. The first special constraints is the optimal volume in 1st MFD (assumed with higher capacity) should be greater than optimal volume in 2nd MFD which can clearly differentiate the MFDs with higher and lower capacity without cut each other. The second one is the volume corresponding to maximum density in 1st MFD should be greater than that volume in 2nd MFD. This constraint made sure that the tail of two MFDs didn’t collide with each other. The last constraint is the optimal volume is greater than a certain volume to represent the full capacity of that MFD. 8. Run the Excel Solver after implementing all objective function, variables and constraints. 9. Now consider number of MFD as three & four. Add necessary variables and constraints. Repeat steps 6.

20

Figure 2-5: Flow chart for determination of number of MFDs After that, total RMSE for each scenario of number of MFDs are measured. By observing RMSE versus number of MFDs figure, it can be seen that with the increase of number of MFDs, the total root mean square error (RMSE) is decreasing. The decrease in RMSE from one MFD to three MFD was rapid but after that the change is very low from scenario of three MFDs to four MFDs. So, three potential macroscopic fundamental diagrams were considered to represent our macroscopic link properties. The first MFD is representing links with higher capacity, indicating urban major roads and highways, the second MFD is representing links with medium capacity, indicating urban minor roads and the last MFD is representing links with low capacity and higher density, indicating urban minor roads suffering from traffic congestion, long queue and spillbacks.

21

Figure 2-6: Flow chart for determination of number of MFDs

First Macroscopic Fundamental Diagram Link numbers 3, 4, 6, 7, 9-12, 15, 18-19, 23, 28, 32, 35, 37-39, 41-43, 45-48, 51, 54-59, 61, 62, 64, 66, 67, 70, 72-74 are included in the first fundamental diagram, which has higher capacity. The calibrated model equation of volume-density relationship is achieved by using non-linear modeling in JMP. The calibrated equation for the first MFD is given in Table 3. The volume-density relationship is shown in Figure 2-5(a).

Second Macroscopic Fundamental Diagram Link numbers 2, 5, 8, 13, 14, 16, 17, 20, 22, 24, 26, 31, 33, 34, 40, 44, 49, 50, 52, 53, 65, 68, 69, 71, 75, 76 are included in the second fundamental diagram, which has lower capacity than the links for the first MFD. The calibrated equation the second MFD is given in Table 3. The volume-density relationship is shown in Figure 2-5(b).

Third Macroscopic Fundamental Diagram Link numbers 1, 21, 25, 27, 29, 30, 36, 60, 63 are included in the third category of fundamental diagram, which has lowest capacity due to spillback characteristics. By observing these link behavior, it can be seen that the links are significantly congested after a certain period and spillback is occurred. The calibrated equation for the third MFD is given in Table 3, and the volume-density macroscopic fundamental diagram is given in Figure 2-5(c). Table 2-4: Calibrated Equation and Value of Parameter for Each MFD for base scenario

MFD Calibrated Equation Free flow speed, uf

(mph) Optimal density, k0

(veh/ hr/lane)

First kk

eqln

37.50)77.35ln(

(2.10) 35.77 50.37

130

140

150

160

170

0 1 2 3 4 5

RM

SE

Number of MFDs

Determination of Number of MFDS

22

Second kk

eqln

57.33)39.28ln(

(2.11) 48.16 33.57

Third kk

eqln

68.31)61.35ln(

(2.12) 35.61 31.68

(a)

(b)

(c)

Figure 2-7: Volume-density macroscopic fundamental diagram: (a) first MFD; (b) second MFD; and (c) third MFD (legends: blue to grey to red).

2.8.5 Evaluation of speed variation on MFD To observe the impact of speed on the link characteristics in MFD, two scenario are observed. In first scenario, the desired speed of each link is determined from their currently permitted speed limit. In second scenario, the speed of each link is assumed 30 mile/hr (50 km/hr) which is not realistic. The volume-density relationship of each scenario are given in figure2- 6.


23

(a)

(b)

Figure 2-8: Volume-density macroscopic fundamental diagram: (a) Scenario 1; (b) Scenario 2; (legends: blue to grey to red)

The probable position of links on MFDs are given in table 2-5. As it is seen that, some of the link location are quite different in this two scenarios due to speed change. As in scenario 2, the speed of all links are same. But when the original speed of each type of road link were imposed, the average volume and density of all links were changed. The change of link location for some links are quite significant as they were situated on different MFDs on those two scenarios. For Example, the speed of link 3 in scenario 2 is 30 mile/hr and it is on second MFD. But the actual speed of link 3 is 45 mile/hr. When the original speed is imposed in scenario 1, the flow is increased due to increase of speed from 30 to 45 mile/hr. and the link is jumped to first scenario.

Table 2-5: Comparison between scenarios in evaluation of speed variation

Scenario MFDs

1st 2nd 3rd

1 3, 4, 6, 7, 9-12, 15, 18-19, 23, 28,

32, 35, 37-39, 41-43, 45-48, 51, 54-59, 61, 62, 64, 66, 67, 70, 72-74

2, 5, 8, 13, 14, 16, 17, 20, 22, 24, 26, 31, 33, 34, 40, 44, 49, 50, 52, 53, 65, 68,

69, 71, 75, 76

1, 21, 25, 27, 29, 30, 36, 60, 63

2

2,6,7,9,16,19,23,26,31,32,34,35,37,38,41,43-

45,47,49,51,52,55,56,58,61,66,68,71-74

3-5,11-15,17,20,22, 28,30,33,39,42,46,48,50,53,

54,57,59,62,64,65,69,76

1,8,10,18,21,24,25,27,29,36,40,60,63,67,

70,75

2.9 Changing Control Strategy As we observe from the microscopic simulation results, there are some scatter characteristics in the MFDs. This can occur due to a non-optimized signal timing plan, unbalanced coordination of the signal phases, improper distribution of traffic flow, etc. To illustrate the impact of control strategies on the shape of the MFD, the signal timing plans are changed for those intersections

Legends: ascending

order

24

having links with high congestion and spillbacks. There are two modified signal control strategies observed - single node and multiple node.

The new control strategies had two main purposes/tasks. The first purpose was to increase the green time in the split for those congested links, so that there is sufficient green time for vehicles to go through the intersections. This will reduce the traffic congestion and spillbacks. The second purpose was to decrease the green time in the split for the perimeter nodes, so that the number of incoming vehicles into the network is reduced. This can also help to minimize the possibility of traffic congestion and spillbacks along the congested corridor. These tasks were applied iteratively to arrive at the best possible signal timing plan for those congested nodes to minimize the scatter characteristics in the MFD. One indication of the success of the task is the shifting of the location of the link points to higher MFD from lower MFD. This indicates an increase of capacity of those congested links, leading to a decrease in link congestion and spillbacks in the overall network. This can also increase the proper coordination among the intersections and decrease the total network delay.

2.10 Modified Traffic Control Parameter There are 30 signal control parameters. All the control parameters were changed to observe the impact on the shape of MFDs (table 2-6). Webster method is used to determine the new cycle length and green times. The Webster’s formula for optimum cycle (WOCL), total effective green, effective green per phase are given below:

n

iY

LC

1

55.10 (2.13)

LCg ot (2.14)

in

i

it g

Y

Yg

(2.15)

Where, Co = Optimum Cycle Length (sec), L = Total lost time (sec) Yi = critical volume/saturation flow (for phase i)

n

iY = Total critical volume/saturation flow

gi = Effective green time for phase i (sec)

gt = Total effective green time (sec)

The peak hour volume for all directions of every intersection in Sioux falls network were collected from traffic volume count in morning and afternoon peak periods. The saturation flow is 1800 vehicle per hour. The default design lost time is also used to estimate Webster’s effective green time for every phase in every nodes.

25

By observing the VISSIM network model, it can be seen that links in second and third MFDs are the congested compare with first MFD in base scenario. The congested links are associated with nodes 9, 10, 11, 12, 16, 17 and 21. The green times of signal timing control in all nodes are recalculated by Webster’s method (Table 2-6 and 2-7) so that there are some increase and decrease in effective green time. The links with increased green times can provide sufficient green time for congested traffic to go through the intersections. As a result, the volume of links are increased.

On the other hand, the links with decreased green time can reduce the number of incoming vehicle into the network. This may cause increase in link density and lead more congestion. There are additional two sub scenarios were observed by increasing and decreasing the Webster’s optimum cycle length of 10 seconds. The green time for each phase in all ndoesfor three scenarios – WOCL, WOCL+10 and WOCL-10 are given in table 2-6 and 2-7. Table 2-6: Signal Control Parameter in Existing and Webster’s method

Node Existing Control Parameters Webster’s method (WOCL Scenario)

Cycle Length

Split Cycle

Length Split

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 98 10 35 23 10 35 23 98 11 77 0 10 3 77 0 18

3 NB 90 51 16 23 18 33 39 65 0 34 22 10 3 36 0 26 3 SB 90 58 32 58 54 26 28 26

4 90 23 34 21 12 12 45 12 21 120 12 77 16 15 12 77 16 15 6 90 62 28 74 33 89 0 16 0 72 0 35 0 54

7 NB 76 40 10 26 40 36 85 0 34 23 28 0 62 0 23 7 SB 76 50 26 20 30 55 0 23 0 32 27 28 0 0

8 90 12 34 14 30 12 34 12 32 66 2 37 13 14 2 37 13 14 9 114 15 52 20 27 67 47 119 14 33 12 61 37 82

10 UH 114 74 40 22 52 40 52 131 57 74 12 52 67 10 LH 114 15 57 42 72 42 123 19 50 54 56 67 10 3rd 114 19 50 29 16 69 16 29 119 21 18 16 64 21 18 79 11 100 10 44 16 30 54 16 30 129 15 44 12 57 57 20 52 12 100 35 40 25 18 57 25 55 18 28 10 18 28 10 13 NB 100 70 30 20 50 30 101 0 76 0 25 19 62 0 20 13 SB 100 70 30 70 111 0 64 0 47 0 82 0 29 14 60 35 25 35 25 103 13 37 6 46 6 38 6 53 16 90 15 30 15 30 15 30 15 30 86 7 46 8 25 9 43 13 22 17 90 12 42 36 12 42 12 24 163 37 50 76 35 47 15 67 18 90 24 38 28 24 38 68 32 16 20 0 45 22 0 0 19 90 14 35 10 31 11 38 10 31 129 5 42 8 74 8 47 24 50 20 NB 90 11 49 30 60 119 12 41 0 65 0 38 0 81 20 SB 90 60 30 11 49 30 119 0 35 0 84 13 42 0 64 21 114 11 47 21 35 15 43 35 21 135 19 50 16 50 15 57 9 54

26

22 114 10 62 10 32 10 62 10 32 99 11 29 11 48 10 32 7 51 23 60 30 30 30 30 125 0 46 0 79 0 58 0 67 24 100 13 49 13 25 13 49 13 25 106 19 39 14 34 12 46 8 41

Table 2-7: Signal Control Parameter for Congested Intersections for WOCL+10sec and WOCL-10sec Scenario

Node WOCL+10sec Scenario WOCL-10sec Scenario

Cycle Length

Split Cycle

Length Split

1 108 12 85 0 11 3 85 0 20 88 10 69 0 9 3 69 0 16 3 NB 75 0 40 25 11 4 41 0 30 55 0 29 18 8 3 30 0 22 3 SB 64 0 31 0 33 0 31 0 0 44 0 21 0 23 0 21 0 0

4 130 13 84 17 16 13 84 17 16 110 11 71 14 13 11 71 14 13 6 99 0 18 0 81 0 39 0 60 79 0 15 0 64 0 31 0 48

7 NB 95 0 38 26 31 0 70 0 26 75 0 30 20 25 0 55 0 20 7 SB 65 0 27 0 38 32 33 0 0 45 0 19 0 27 22 23 0 0

8 76 2 42 15 16 2 42 15 16 56 2 31 11 12 2 31 11 12 9 129 15 36 13 66 0 40 0 89 109 13 30 11 56 0 34 0 75

10 UH 141 0 62 0 79 12 56 0 72 121 0 53 0 68 11 48 0 62 10 LH 133 20 54 0 59 0 60 0 72 113 17 46 0 50 0 51 0 61 10 3rd 129 23 20 17 69 0 23 20 86 109 19 17 15 58 0 20 17 73 11 139 16 48 13 62 0 62 21 56 119 14 41 11 53 0 53 18 48 12 65 21 33 0 12 21 33 0 12 45 15 23 0 8 15 23 0 8 13 NB 111 0 83 0 28 21 68 0 22 91 0 68 0 23 17 56 0 18 13 SB 121 0 70 0 51 0 89 0 32 101 0 59 0 43 0 75 0 27 14 113 14 40 7 51 6 42 7 59 93 12 33 6 42 5 34 5 48 16 96 8 51 9 28 10 48 15 24 76 6 41 7 22 8 38 12 19 17 173 39 53 0 81 37 49 16 72 153 35 47 0 72 32 44 14 63 18 78 37 18 23 0 52 25 0 0 58 27 13 17 0 39 19 0 0 19 139 5 45 9 79 9 50 26 54 119 5 39 8 68 7 43 22 46 20 NB 129 13 45 0 71 0 41 0 88 109 11 38 0 60 0 35 0 74 20 SB 129 0 38 0 91 14 46 0 69 109 0 32 0 77 12 39 0 59 21 145 20 54 17 54 16 61 10 58 125 17 46 15 46 14 52 9 50 22 109 12 32 13 53 11 35 7 56 89 10 26 10 43 9 28 6 46 23 135 0 50 0 86 0 63 0 73 115 0 42 0 73 0 53 0 62 24 116 21 42 15 37 13 50 9 44 96 18 35 12 31 11 41 7 37

2.11 Results and Analysis After changing the signal timing plan of all nodes for three scenarios – WOCL, WOCL+10 and WOCL-10, the MFDs are significantly different from the base scenario. After collecting the simulation results (given in table A.4, table A.5, table A.6 in Appendix A), the Underwood model is used to calibrate the volume-density relationship. The calibrated equation for speed-density relationship is:

27

0k

k

f keuq (2.20)

The calibrated free flow speed and optimal density for all three modified scenarios are measured by using excel solver. The volume-density relationship in this model is shown in Figure 2-9(a).

2.11.1 Modified macroscopic fundamental diagrams The new volume-density MFDs using the Webster’s optimum cycle length are provided in Figure 2-9(b) and the links included in each MFD are shown in table 2-9. In Figure 2-9(b), it can be clearly noticed that many points are shifted from their previous position. There are three MFDs in every scenario (shown in Figure 2-9(b)). The MFDs for WOCL, WOCL+10sec and WOCL -10sec are shown in figure 2-9, 2-10 and 2-11 respectively. Table 2-8: Link number under each MFDs in WOCL, WOCL+10sec and WOCL-10sec scenarios

Scenario MFDs

1st 2nd 3rd

Base

3, 4, 6, 7, 9-12, 15, 18-19, 23, 28, 32, 35, 37-39, 41-43, 45-48, 51, 54-59, 61, 62, 64, 66, 67, 70, 72-74

2, 5, 8, 13, 14, 16, 17, 20, 22, 24, 26, 31, 33, 34, 40, 44, 49, 50, 52, 53, 65,

68, 69, 71, 75, 76

1, 21, 25, 27, 29, 30, 36, 60, 63

WOCL 4-6,11-13,30,31,34,37-

39,41,43,44,48,49,51,54,55,58,61,76

2,3,7,9,10,14-17,19,20,22-24,26,32,33,35,42,45-

47,50,52,56,57,62,64-66,68,69,71-74

1,8,18,21,25,27-29,36,40,53,59,60,63,67,

70,75

WOCL+ 10sec

6,7,10,11,13,34,37-39,41,43,44,48,55,59,61,6

4,67,70

2,3,5,9,12,14-17,19,20,22,23,26,28 ,30-33,35,42,45-47,49-53,56-58,62,65,66,68,69,71-74,76

1,4,8,18,21,24,25,27,29,36,40,54,60,63,75

WOCL-10sec

2, 3,6,7,10,13,17-19,23,28,31,32,37-39,41-44,51,54,55,58,61,64,67,7

0,73

5,9,12,14-16,20,22,26,30,33-35,45-47,49,50,52,53,56,57,59,62,65,66,68,

69,71,72,74,76

1,4,8,11,21,24,25,27,29,36,40,48,60,63,75

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(a)

(b)

(c)

(d)

Figure 2-9: Modified macroscopic Fundamental Diagram for WOCL Scenario: (a) volume-density; (b) first MFD; (c) second MFD; and (d) third MFD (legends: blue to green to red).

(a)

(b)

Legends: ascending

order


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(c)

(d)

Figure 2-10: Modified macroscopic Fundamental Diagram WOCL+10sec Scenario: (a) speed-density; (b) volume-density; (c) first MFD; (d) second MFD; and (e) third MFD (legends: blue to green to red).

(a)

(b)

(c)

(d)

Figure 2-11: Modified macroscopic Fundamental Diagram WOCL-10sec Scenario: (a) speed-density; (b) volume-density; (c) first MFD; (d) second MFD; and (e) third MFD (legends: blue to green to red).

Legends:

ascending order

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2.12 Discussion The state of roads can be determined as uncongested or congested based on their density. In volume-density fundamental diagram, the links with density located left portion (shown as zone I in figure) are called uncongested. Also the density of congested links are located at right side in zone III (figure 2-11). The zone II is called sweet spot where the links have their optimum density based on capacity. In this study, the condition of links were changed in the range from uncongested to congested level due to the change of control strategies. The effect of the changes in various scenarios are discussed below:

Figure 2-12: Macroscopic Fundamental Diagram [3]

2.11.1 Change of Link location between MFDs

After changing the signal timing plan of all nodes based on Webster’s optimum cycle method, it can be seen that the location of uncongested link 49 was shifted from old second MFD to new first MFD. The green time of link 49 was increased on node 19 in new scenario. The extra green time allows more time for those traffic to clear the intersection which were stuck in the congestion in base scenario. As a result, the density of this link is significantly decreased and the location of link point shifted to zone II in new first MFD (table 2-9).

Table 2-9: Change of Link Location among MFDs

Scenario Base WOCL Scenario

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MFDs

From above results and analysis after changing the signal control parameter of all nodes, it can be observed that there is a significant difference in the composition of the MFDs between the base scenario and three modified-control scenarios – WOCL, WOCL+10 and WOCL-10, etc. It is found front the comparison of the scenarios that there is less scatter in the modified MFDs in compare with base scenario based on RMSE values. In addition, some points shifted from one MFD to another MFD due to this change of control parameters. The links with shifted location between MFDs are given in table2-10. Table 2-10: Change of Link Location among MFDs

Sceanrio Jump/Drop Jump/Drop between MFDs in compare with base scenario

Link Number

WOCL Drop

1st MFD to 2nd MFD 3,7,9,10,15,19,23,32,35,42,45-

47,56,57,62,64,66,72-74 1st MFD to 3rd MFD 18,28,59,67,70 2nd MFD to 3rd MFD 8,40,53,75

Jump 2nd MFD to 1st MFD 5,13,31,34,44,49,76 3rd MFD to 1st MFD 30

WOCL+10 Drop

1st MFD to 2nd MFD 3,9,12,15,19,23,28,32,35,42,45-

47,51,56-58,62,66,72-74 1st MFD to 3rd MFD 4,18,54, 2nd MFD to 3rd MFD 8,24,40,75

Jump 2nd MFD to 1st MFD 13,34,44

WOCL-10 Drop

1st MFD to 2nd MFD 9,12,45,35,45-

47,56,57,59,62,66,72,74 1st MFD to 3rd MFD 4,11,48, 2nd MFD to 3rd MFD 24,40,75

Jump 2nd MFD to 1st MFD 2,13,17,31,44, 3rd MFD to 2nd MFD 30

In scenario of WOCL, the cycle length of node 17, 20, 21 and 23 increased with increasing the green time of phases based on traffic volume data from field survey. For example, in node 21, the green time of phase 4 is increased so that the traffic on link 44 had more to clear the intersection and the possibility of congestion on that link was decreased. As a result, the density of link 44 was decreased and traffic volume on that link is increased which leads the shifting of the link from old

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second MFD to new first MFD in WOCL scenario. Similarly the link number 5, 13, 30, 31, 34, 44, 49 and 76 are improved by jumping from old lower MFD to new higher MFD, showing they have a greater capacity.

Beside this, the cycle length of node 3, 12 and 16 were decreased. Especially the significantly decreased cycle length of boundary nodes 3 and 12 cause less incoming vehicle into the network due to decrease of green time in corresponding phases. So, the traffic volume on the associated links near the nodes were decreased. This also helped to decrease the density of the links and improve the link condition from traffic congestion. On the other hand, the link flow in link number 46 and 56 were decreased due to reduction in cycle length of node 3 and 8. This is why those links were dropped due to traffic congestion from higher MFD to lower MFD. In addition, the link average vehicle delay and link average stop delay are significantly changed due to variation of link performance (Table 2-11). Table 2-11: Total vehicle delay and total stop delay in every scenario

Scenario Total Vehicle Delay (veh-sec) Total Stop Delay (veh-sec)

Base 7625.39 10790.88

WOCL 7165.99 6328.28

WOCL+10 6785.75 5657.01 WOCL-10 7278.63 6417.27

Due to further increase of cycle length of those nodes by 10 seconds from WOCL, it can

be observed that the green time of those congested links are increased more to facilitate the congested traffic. Thus, the link number 24 on node 11 are shifted from old second MFD to new first MFD in WOCL+10 seconds scenario. The total vehicle delay and stop delay are also reduced in this scenario compare with base and WOCL scenarios (table). On the other hand, due to decrease the cycle length of those nodes by 10 seconds from WOCL, the effective green time of some phases with congestion were reduced. As a result the density of those links were increased. For an example, the effective green time of phase 2 on node 6 along link 4 were reduced in compare with both base and WOCL scenario. AS a result, the density of link 4 were increased and the location of the link was shifted from old first MFD to new third MFD.

When the signal timing plan in congested intersections is changed, medium- or low-level traffic congestion is reduced, and the capacity of those links are increased to the extent that the links can be considered as having a higher capacity. The change of signal timing parameter also have some limitation when the links have long queue and spillback. For this reason, the link number 1, 21, 25, 27, 29, 30, 36, 60, 63 in base scenario were considered congested links having traffic volume less than 200 vehicle per hour. Thus, the number of congested links in the third modified MFD is fewer, and the capacity of those links is lower.

Because the O-D data are used for traffic flow, those O-D trips impact the proposed

strategy. If any particular O-D route has higher volume, and the route consists of congested links, the link congestion can be decreased, and the possibility of spillback can be reduced, which will

33

lead to a decrease in travel time between origin and destination nodes. This also may lead to an improvement in link capacity and a decrease in delay for each O-D trip.

2.13 Conclusion and Future Work An MFD can help traffic engineers understand the important characteristics of a large network. It can magnify the important factors, which are related to the uniform traffic flow, roadway capacity, traffic congestion, possibility of accidents, possible spot for bottlenecks, etc. The results discussed above signify the effects of control strategies on the number and composition of MFDs in a given network. It could be that well-designed control strategies can reduce traffic congestion and significantly improve the capacity of congested links in a large urban network. Well-designed control strategies can also increase the proper coordination among intersections and decrease the travel time. All of these can lead the decrease in overall network delay.

For future work, emergency vehicles will be introduced into the network in various frequencies, and their effect on MFDs will be observed. The impact of emergency vehicles on MFDs is important for understanding the characteristics of a large urban network in an emergency situation.

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and switching signal plans controllers. in Control Conference (ECC), 2013 European. 2013: IEEE.

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12. Daganzo, C.F. and N. Geroliminis, An analytical approximation for the macroscopic fundamental diagram of urban traffic. Transportation Research Part B: Methodological, 2008. 42(9): p. 771-781.

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19. Geroliminis, N. and J. Sun, Hysteresis phenomena of a macroscopic fundamental diagram in freeway networks. Transportation Research Part A: Policy and Practice, 2011. 45(9): p. 966-979.

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3 USING MACROSCOPIC FUNDAMENTAL DIAGRAMS TO EVALUATE THE EMERGENCY VEHICLE PREEMPTION OPERATION IN URBAN NETWORK

Abstract Modern traffic signal control systems provide emergency vehicle preemption (EVP) capabilities using advanced communication technologies. While EVP strategies have been widely implemented in single intersection, few research efforts have studied the transition methods in corridors. This paper presents comprehensive evaluations of EVP operations in a network with freeways and urban roads. For the evaluation of the traffic flow characteristics, macroscopic fundamental diagram (MFD) is considered as performance measure. The MFD plays an important role to characterize and evaluate network performance by analyzing the spillback and scatter characteristics in a large network. There are three different scenarios are analyzed – base, low and high volume of emergency vehicle. The proposed scenarios are applied to develop an idea to evaluate the EVP operation using the characteristics of MFDs. The results show that the volume of links along the emergency route is increased and the volume of other links closed to the emergency route is decreased due to preemption. This incident is noticed by observing the shifting of point locations between MFDs which has significant effect on the level of service of the links and the overall network traffic flow operation.

36

3.1 Introduction Emergency vehicle traffic signal preemption system is a promising approach in modern traffic engineering operation and management. Due to the rapid growth in number of populated areas and vehicles, the traffic congestion and transportation delay on urban arterials are increasing worldwide causing hindrance to the improvement of the safety and efficiency of transportation in emergency situation [121-124]. To solve the problem of emergency vehicles path delays, several solutions were introduced over the years, known as “Pre-emption Systems” [124-126]. The EVP systems are designed to provide emergency vehicles a green light on their approach to a signalized intersection while providing a red light to conflicting approaches [123]. In basic preemption operation, the emergency vehicle is detected by sensor at each controller and each individual controller switches to green signal which is held until it exits the intersection [127, 128].

The EVP can help to reduce delay time, improving safety and minimizing the cost of transportation [121, 127]. The signal preemption can also reduce conflicts, improve emergency response times, thereby reducing confusion and the number of emergency vehicle crashes [121]. Researchers from past studies are found to apply EVP on isolated signalized intersection or corridor mostly [53-55, 65, 76, 129-133]. In this paper, we illustrate the EVP operation on an urban area network. This will help to characterize the traffic flow on an urban area network in an emergency situation. To have a better characterization, the Macroscopic Fundamental Diagram (MFD) is used.

Due to large number of urban road links and signalized intersections in an urban area

network, modeling traffic flow operation of individual links can be an strenuous task [1]. To ease the procedure, the network can be analyzed in a macroscopic level by using MFD. The concept of MFD has emerged to establish a relationship between volume and density from empirical and simulated data in homogeneous urban network regions during the last decade, [3]. Researchers from previous studies found the character of the MFD mainly dependent on the network topology, traffic flow, peak/off peak period, vehicle route choice, the signal timing plans of the intersections, rate of incoming/outgoing traffic [4], and the infrastructure characteristics [5]. Though Homogeneous networks were found to have a well-defined MFD, heterogeneous networks might not have a well-defined MFD [6].

The EVP is a challenging phenomenon for researchers and practitioners. There are various

types of strategies found for EVP. The strategies found from previous studies evolves with signal timing control, vehicle routing, introduction of separate lane, connected vehicle and vehicle-infrastructure communication technology [134]. To have a better understanding of EV operation on a large network, a MFD can be very useful. For a large urban network, a MFD can evaluate the performance of the network by observing the changing of macroscopic link properties in the diagram [3].

3.2 Objective The objective of this paper is to evaluate the performance of the EVP operation on an urban network. The paper also tests the hypothesis that there are significant change of capacity in different categories of road due to EVP operation. For the help of better analysis of urban network, MFD is introduced to evaluate the effect of EVP operation. As each MFD defines a different class of operation on the network, it will be easier to compare the change of capacity in network links.

37

In this study, three scenario are implemented based on volume of EV: zero, low and high volume. In essence, an engineer might need to know the condition of traffic operation during EVP operation to accommodate and provide higher class of service for EV routes. The authors therefore aim to cluster the volume-density plot into few different MFDs, and identify the links that fall into each MFD. Since the capacity on network links can be changed by introducing different volumes of EV, the authors aim to illustrate the concept by observing the change of link point location from one class of performance to another during EVP operation. 3.3 Literature Review Most of the previous EVP studies deal with isolated signalized intersection [53-55, 65, 76, 129-132]. Some of the studies imply EVP strategies on corridor with several intersections [58]. On the previous studies, the EVP strategy parameters are usually set to minimize the intersection delay [53, 135]. The maximum allowable green extension and the minimum green period for the cross traffic are considered as preemption strategy parameter [53] . Sometimes a queue can be prioritized if queue length is greater than specified value [54, 55] in a large network. It is really important to reduce the response time of emergency vehicles to reach their destination at the quickest possible time while maintaining safety for all the users [136, 137]. On the other hand, minimizing the impacts of emergency vehicle operation on normal traffic is equally important to avoid the grid-lock caused by emergency accidents [138, 139].

The EV route plays an important role in a wide area network. To estimate the shortest route for emergency vehicle, Dijkstra's algorithm is used [60, 67, 68]. The dynamic preemption module sequentially activates the preemption procedure for the intersections on the selected route depending on the direction and location of an emergency vehicle [60]. For multiple priority requests, a first-come-first-serve rule is used [70]. A multilayer fuzzy model can be used to determine the degree-of-priority based on two emergency vehicle preemption factor - demand intensity and preemption influence intensity [68]. In a large network, it is very important to avoid the links with traffic congestion and spillbacks for EV. The MFD can help us to identify those links.

The Fundamental Diagram (FD) is one of the most broadly used theories in the field of traffic flow. In a large-scale network, the relationships between flow and density in all links is known as the MFD. Under homogeneity condition, MFD is defined as a low scatter unimodal relationship between network flow and network accumulation. The idea of an MFD was originally proposed by Godfrey in 1900 [8], but the empirical observation of its existence in a large-scale urban network is recent [3, 18]. Researchers claimed that the evaluation of traffic congestion, effectiveness of traffic management systems, causes of accidents, possibility of bottlenecks, and effect of emergency vehicle preemption can then be conducted for the whole network, and not necessarily at each individual link [3, 18].

In this paper, we basically observe the EVP operation in an area wide network. It is very difficult to analyze each of the link properties to characterize the effect on large network [1]. Thus, there is no standard procedure for evaluating the EVP operation on a large network. To solve this problem, we introduce MFD to analyze and compare the link properties in a macroscopic level. From MFD analysis, we can identify the links with traffic congestion and spillback possibilities

38

and average link speed. Researchers from previous studies used operating speed, average travel time and spillback possibility as performance indicators for preemption and priority services [56, 58]. In the case of potential queue spillback of the off-ramp, congestion level in the upstream is estimated [59] in a large network. Researchers also used an index that quantifies level of congestion as the travel cost of each link [60].

3.4 Study Area To evaluate the area wide preemption by using MFD, microscopic simulation is conducted. For that reason, Sioux Falls is considered as our study area to fulfill the objective (see Figure 3-1(a) and 3-1(b)). The reason behind this consideration is the Sioux Falls is widely used by researchers in many fields including traffic engineers [140, 141]. As most of the researchers already knew the network characteristics, it would be easier for them to understand our proposed approach. Thus, they can easily compare our findings with theirs studies and imply this on their future studies.

(a)

(b)

39

(c)

Figure 3-1: Sioux falls network; sources: (a) google map (b) reproduced by Hai Yang and Meng Qiang [81] (c) Sioux Falls Network in VISSIM

3.5 Simulation Input Data For fulfillment of our objective, a macroscopic simulation has to be conducted to develop MFD and to make analysis and comparison of the macroscopic link properties based on that. For this reason, VISSIM 7.0 was chosen as a microscopic, time-step and behavior-based traffic simulation software used in this study. Various traffic operations under constraints such as lane configuration, traffic composition, speed limits, traffic signals, and time of day can be analyzed by using VISSIM. The VISSIM model setup required the input of geometric, traffic control, and traffic flow data for the study corridor (Figure 3-1(c)). Highlights from the data collection and field observations relevant to the VISSIM model development are discussed below.

3.5.1 Geometric Data For building the Sioux Falls network, geometric data is an essential element. The features of VISSIM 7.0 already consists of the number of lanes, lane additions, lane drops, auxiliary lanes, highway curvature, and intersection geometry. Additional geometric information for the Sioux Falls network study was obtained from scaled aerial photographs in bitmap format downloaded from Google Maps [91] and from field observations [90].

40

The Sioux Falls network consists of 24 nodes and 76 links [90]. The lane configurations and other details of geometric data were taken from aerial photographs and were confirmed based on field observations [90] (given in table A.1 in Appendix A). For the ease of calculation, we have to number all the links. The eastbound, westbound, southbound and northbound links are numbered as 1 to 16, 17 to 32, 33 to 54, and 55 to 76, respectively (Figure 3-1(b)).

3.5.2 Traffic Control Data Traffic signal timing plan is an essential part for implementation of real characteristics of traffic flow on a large urban network. The study area has 23 signalized intersections. The location of intersection control was identified using Google aerial map [91] and confirmed using the Sioux Falls signalized intersection network map provided by the Traffic Engineering Department of the city of Sioux Falls [92].

The required traffic signal timing sheets for the signalized intersections were obtained from the Traffic Engineering Department of the city of Sioux Falls [92] and the signal timing information was fed into VISSIM. The detail signal timing plan for each intersection includes the ring barrier diagram, cycle length, splits, offset, coordinated phases, and traffic patterns of each intersection.

3.5.3 Traffic Flow Data To implement the traffic flow in simulation, traffic flow data are necessary for our study area. In this study, Origin destination (O-D) trip data (given in table A.2 in Appendix A) are used as traffic flow data relevant to the micro-simulation model’s development. The O-D data are obtained from the dynamic assignment study by A. Karoonsoontawong et al. [140].

All links have the capacity of 600 vehicle per hour, and have a free flow speed of 50 mph. The study period is a peak hour with 528 O-D pairs. All O-D demands are determined from 450 vehicle per hour multiplied by the factors collected from the dynamic assignment study [140]. The total number of vehicle trips is 178,542 [140]. The vehicle routes are determined according to the O-D data. The default distribution of vehicle types given in VISSIM was used.

3.6 Emergency Vehicle Preemption Strategy Signal timing control for EV activation on the network depends on configuration of optimal preemption system parameters and on design of optimal timing plans.

3.6.1 EV Input For implementation of EV flow, vehicle input is introduced in link 82 near node 3. In this study, we observed two kind of scenarios based on different level of EV input volume – low and high. The vehicle input for low and high EV scenarios are 18 and 36 vehicle per hour respectively.

3.6.2 EV Route To find out potential emergency vehicle route, the location of hospitals and residential areas are identified in google map (See Figure 3-2). As it is seen in the figure 3-2, there are many potential

41

emergency route. A potential EV route was taken into consideration in this study (Figure 3-2). For preemption system, an EV route is introduced in the Sioux Falls network (see Figure 3-2 (c)). On the proposed route, the EV got into the network through node 6, passes through the nodes 8, 16, 17, 19, 20 SB and got out from the network by node 21. The links along the EV route direction are 46, 47, 48, 49, 50 and 32. The purpose of choosing this route is having all kinds of links based on MFDs which helps us to observe the effect on different types of links.

(a) Hospital Zones (HZ)

(b) Residential Zones (RZ)

(c) Route 1

Figure 3-2: Proposed EV route (path and nodes colored as yellow)

3.6.3 Vehicle Class and Types To differentiate the EV from normal traffic, a vehicle class and type is introduced as EV in VISSIM. The category of EV is assigned as high goods vehicle (HGV). The default data of static

42

and dynamic characteristics for HGV are used. For identification of EV during the simulation, the color of EV is assumed as red where white color is used for normal traffic.

3.6.4 Priority and Preemption Activation To activate the preemption, the signal timing plans are modified in every associated intersection along the EV route (see Figure 3-3(a)). In EVP operation, there are three stages – transition in, serving/dwell phase and transition out. Transition in includes terminating vehicle phases, overlaps, and pedestrian phases not called for in the preemption operation. However, yellow and red clearance intervals are not supposed to be shortened or omitted during transition in or transition out [44]. For “transition in”, the phases which were possibly conflicted with EV route direction are included in “Zero SG Green” section so that signal groups flagged for this parameter can be terminated immediately out of “Green” (zero time) if not permitted by the current preempt state [142].

For the dwell state, the signal groups which are permitted for EVP operation were used as all other signal groups will be terminated and omitted after the preempt Start Green timing has been satisfied. The preemption operation will always enter the dwell state and remain there until all associated Preempt Inputs are inactive [142]. The “Start Green” is assumed one second to execute the preemption as soon as possible after detection of EV.

During Transition Out (exit mode), the intention is to return the system back into

coordination with minimum effects on traffic. According to NEMA TS-2 standard [143], transition out needs to reach a preprogramed point of the normal signal timing plan. There are three types of exit mode – normal, next and in step. In normal exit mode, the Preemption will exit according to the defined exit vehicle signal groups. The next exit mode is activated when the Preemption will exit to the first signal groups following the signal groups that were timing when the preemption was activated. If any signal groups are flagged in the exit signal group parameter, the exit signal group decision will be limited to those signal groups that are flagged. The in step exit mode is defined as the Preempt will exit into the coordination pattern which is being commanded at the end of preempt. The exit mode is assumed as “Next” because the average queue length of other links are varied with time. So, it’s hard to decide which phase should be green during transition out of preemption [142].

As there is only one EV route direction, one preemption input is introduced [142]. In the

preemption section of ring barrier diagram (RBC) settings, the signal phases along the EV route were included and enabled to change during EVP for adjustment with other signal phases.

43

(a)

(b)

44

(c)

Figure 3-3: (a) Preemption setting (b) Detectors Settings (c) Detectors

3.6.5 Detection Method Each intersection may have queued vehicles which must be cleared to enable free movement of the emergency vehicle. The location of upstream check-in detector are dependent on some important factors such as queue length, vehicle speed, duration of yellow and all red length, etc. The delay on EV route is also an important factor to optimize the location of check-in detector in upstream [144]. There already exist several commercial systems that allow EVs to request and receive green indications. Leading examples of traffic signal preemption system include OPTICOM system from 3M Corporation Inc. [99, 145, 146], STROBECOM system from Tomar Electronics Inc. [147], and MIRT system from Platinum One Net Inc. [148]. The communication range of EMTRAC system between intersection-approach zones is up to 3,600 ft. without using range-extending equipment [149]. The STROBECOM system can preempt intersections at a range from 200 to 2500 feet [147]. The Mart system can turn the traffic lights green from up to 4000 ft [148]. Previous testing of infrared detection systems concluded that average distance for optical detection was ranging from 204.73 to 802.13 ft. [150]. In an evaluation study of preemption and priority, the surveillance detector was located 578 feet from the stop bar at the intersection and acted as a “check-in” detector [151]. On the contrary, installation in Illinois placed advance loop detectors approximately 250 feet upstream [152-154]. From the previous studies, it can be observed that the location of check-in detectors for EV detection should be determined by considering maximum queue length, travel time from upstream detector to stop bar and time required to activate the green signal after EV checked in. The basis of active priority is the selection of detection type of the EVs [53, 54, 66]. There are two kinds of detector that can be used in VISSIM: presence and check in/out. Both detectors are to be used within VISSIM for the corresponding Preempt input. Presence, check in and check

45

out detectors have hard coded number as 401-410, 411-420 and 421-430 respectively. In this study, Check in/out detectors are used. As the authors have two emergency routes, so for every intersection signal control, check-in detectors coded as 411 number and check-out detectors coded for 421 are implemented in upstream and downstream respectively (see Fig. 3-3(b) and 3-3(c)). The detectors are not assumed as calling point detectors so there is no lateness parameter. The values of delay call time and check out limit time are assumed zero so that the EV has no time limit to check in or out. In this study, The STROBECOM system was considered as emergency vehicle detection system. In this system, preempt intersections are usually equipped at a range from 200 to 2500 feet. So, by the check-in detectors are implemented 2500 feet far from the signal head corresponding to each intersections along the EC route. The corresponding yellow and red signal timing of each intersection related to EV route and the time required traveling the distance between signal head and check-in detection is also considered to determine the minimum upstream distance of check-in detectors for double check so that the EV don’t have to wait for extra seconds to clear the intersection. The check-out detectors are placed around 100 feet downstream for passing the intersection safely (Fig. 3-3(c)).

3.7 Goals of Model Calibration Even if the field data are used to develop a realistic model, there is always a minute level of disparity between simulation data and real data. For that reason, the model obtained from simulation data has to be calibrated to obtain the best match possible between model performance estimates and existing single regime macroscopic diagram models. It may be noted that there are no universally accepted procedures for conducting calibration and validation for complex transportation networks [8]. The responsibility of the modeler is to implement a suitable procedure which provides an acceptable level of confidence in the model results. There are four renowned single regime models considered for calibration: Greenshileds, Greenburg, Underwood, and Northwestern Group.

3.8 VISSIM Model Development To develop the model, the Sioux Falls roadway network was originally traced over a scaled aerial photograph imported into VISSIM. The number of lanes, location of lane additions and drops, the frontage road intersections and other roadway geometry were confirmed by Google Maps [91] and Transportation Network Test Problems [90]. Other additional detail such as vehicle input, speed limits and traffic signal timing plans were incorporated into the VISSIM network for better reflection of field conditions. It was found that not all default VISSIM input parameters represented study area conditions, and some needed to be adjusted to replicate reality.

3.9 Results and Analysis After implementing all geometric data, traffic control data, and O-D trip data for each intersection, the VISSIM model was run to simulate one peak hour period without warm up period. In this study, the total simulation period is 4500 seconds. First 900 seconds are assumed as warm up period because it is observed that time required to travel longest origin-destination distance is around 700-800 seconds. All macroscopic link properties are collected for the range of 900 to 4500

46

seconds. The macroscopic link properties for each link such as volume, speed, density, and delay from VISSIM were collected. The vehicle delay and stop delay were also calculated using vehicle travel time measurements. The output data were exported into Microsoft Excel from VISSIM (given in table A.3 in Appendix). The database has over thirty thousand rows, which is quite huge. To simplify the calculation for determining the macroscopic properties for each link, the average value for each link properties were collected.

3.9.1 Base Data Scenario After the simulation, the MFD was developed by using the macroscopic data to illustrate the base scenario. By observing the Figures 3-4 (b), it can be noticed that most of the points lie 0 to 800 vehicles per hour in volume and 0 to 250 vehicles per mile in density. But some of the points, like link 7, are placed on the upper level of the graph, because the volumes of the links are high, possibly indicating major road with higher traffic. The network is heterogeneous type which means the characteristics of links, number of lane per link, link density, vehicle input are different from each other. All the input data are collected from field survey to develop a realistic heterogeneous network. By observing the volume-density fundamental diagram, it can be seen that the link points are very scatter with increase of higher density. So, it is very difficult to represent the relationship of a heterogeneous network using one MFD curve in traditional way [3, 18].

In this study, multiple MFDs will be introduced by differing their peak level instead of

traditional way. So, MFD with higher peak represents link with higher capacity. The number of MFDs will be selected based on scatter characteristics of volume-density fundamental diagram for a specific dataset in such a way so that each MFD will be the best reprehensive curve of surrounding link points. The link points selected for each MFD are also dependent on which calibration formula being used. By observing figure 3-4(b), three potential macroscopic fundamental diagrams can be observed based on the VISSIM database which are best representative for their closet surrounding link points. The first MFD is representing links with higher capacity, indicating urban major roads and highways, the second MFD is representing links with medium capacity, indicating urban minor roads and the last MFD is representing links with low capacity and higher density, indicating urban minor roads suffering from traffic congestion, long queue and spillbacks.

47

(a)

(b)

Figure 3-4: (a) Sioux Falls network (b) Volume-density fundamental diagram for base scenario (legend: MFD 1: red, MFD 2: green, MFD 3: blue)

3.9.2 Initial Calibration To determine the best fit model, four different models were compared: Greenshileds, Greenburg, Underwood and Northwester Group, by using VISSIM macroscopic output data in JMP statistical software [107]. Table 16 represents the equations, parameters and root mean square error (RMSE) of each single regime model. Also the variations of speed with density in each model are shown in Figure 3-5. Table 3-1: Calibrated Equation, Value of Parameter and Root Mean Square Error for Each Model

Model Name Calibrated Equation

Optimal speed,

u0 (mph)

Free flow

speed, uf

(mph)

Optimal density, k0 (veh/ hr/lane)

Jam density, kj (veh/hr/lane)

Root Mean

Square Error

(RMSE)

Greenshields

75.23210

2kkq (3.1) 10.00 232.75 192.68

Greenburg

kkq

72.220ln16.8 (3.2) 8.16 220.72 132.58

Legends: ascending

order

48

Underwood

50.3225.55

k

keq (3.3) 55.25 32.50 83.31

Northwestern 2

23.252

1

38.46

k

keq (3.4) 46.38 25.23 99.15

Where: u = Speed (miles/hour), k = Density (vehicles/mile), u0 = Optimal speed, uf = Free flow speed, k0 = Optimal density and kj = Jam density

(a)

(b)

(c)

(d)

Figure 3-5: Volume-density fundamental diagram: (a) Greenshileds model; (b) Greenburg model; (c) Underwood model; and (d) Northwester group model (legends: blue to grey to red). In comparison among the models (Fig. 3-5), it can be observed that the Greenburg model is very poorly fitted. But the diagrams for the Greenshields, Underwood, and Northwestern models are very well fitted based on data. By comparing the root mean square error (RMSE) among Greenshields, Underwood, and Northwestern models, it is observed that RMSE is the least for the Underwood model. Thus, the Underwood model is the best fitted for the volume-density relationship in this network model.

kk

ku f

eqln)ln(

0

(3.5)

Legends: ascending

order

49

Where, free flow speed, uf = 55.25 mile/hour and optimal density, ko = 32.50 vehicle/ mile/lane. Now, the authors have to develop the MFDs based on the calibrated equation and simulation data. From our previous study, it is observed that a number of MFDs exist in a given network, indicating the existence of different levels of service on different network routes [155]. After categorization of different capacities of links and development of corresponding MFDs, the authors analyzed the MFDs to get better understanding of traffic flow characteristics of an urban network. The calibrated values of free flow speed and optimal density are calculated for each scenario using JMP statistical software. The calibrated equation of volume-density relationship for each scenario are given in table 3-1 for base scenario.

3.9.3 Determination of Number of Macroscopic Fundamental Diagram The network is heterogeneous type which means the characteristics of links, number of lane per link, link density, vehicle input are different from each other. All the input data are collected from field survey to develop a realistic heterogeneous network. By observing the volume-density fundamental diagram, it can be seen that the link points are very scatter with increase of higher density. So, it is very difficult to represent the relationship of a heterogeneous network using one MFD curve in traditional way [3, 18]. Instead of only one MFD, multiple MFDs will be introduced by differing their peak level. So, MFD with higher peak represents link with higher capacity. The number of MFDs will be determined based on scatter characteristics of volume-density fundamental diagram for a specific dataset in such a way so that each MFD will be the best reprehensive curve of surrounding link points. The link points selected for each MFD are also dependent on which calibration formula being used. Here, different number of MFDs were introduced to determine how many MFDs were perfect to represent our macroscopic link data. For performance measurement, root mean square error were considered to compare among the cases of different MFDs (figure 3-6). The following steps are used for this process: 1. As the output dataset was huge and difficult to use in excel solver, we choose some representative points from each of the links in such a way so that the chosen points of one link are representing the whole scatter characteristics of that link. 2. Input data were link number, density and observed volume from simulation results. As Underwood model is the best calibrated model for this dataset, the estimated volume corresponding to each density were calculated by this model. 3. Assume a link parameter for each link (L1, L2,….., L76) to determine which link is belong to which MFDs. 4. Consider the output macroscopic link data are representing by two MFDs. 1st MFD and 2nd MFD were representing as 1 and 2. 5. Assuming one output density data of link 1 is D1. To determine the estimated volume by Underwood model for each density data, use “If L1 = 1, then, D1 belongs to 1st MFD; otherwise for L1 = 2, then, D1 belongs to 2nd MFD. This way, all the estimated volume were calculated. Initially, All L values were assumed as1. 5. Determine total root mean square error (RMSE) by summing RMSE for each MFDs. 6. Now Excel Solver was used to minimize the RMSE along with which link belongs to which MFDs. The model objective function, variables and constraints were given below:

50

Objective Function: Minimize RMSE = SQRT [Σ(square errors between observed volume and estimated volume)/(Number of data points)] Variables: uf 1, uf 2 = free flow speed for each MFD, ko1, ko1 = optimal density for each MFD L1, L2,…., L76 = link parameters for each link Constraints:

2,....,,1 7621 LLL

75,0 21 ff uu

264,0 21 oo kk

7. Two other special constraints were also considered assuming 1st MFD with higher capacity. The first special constraints is the optimal volume in 1st MFD (assumed with higher capacity) should be greater than optimal volume in 2nd MFD which can clearly differentiate the MFDs with higher and lower capacity without cut each other. The second one is the volume corresponding to maximum density in 1st MFD should be greater than that volume in 2nd MFD. This constraint made sure that the tail of two MFDs didn’t collide with each other. 8. Run the Excel Solver after implementing all objective function, variables and constraints. 9. Now consider number of MFD as three & four. Add necessary variables and constraints. Repeat steps 6. After that, total RMSE for each scenario of number of MFDs are measured. By observing RMSE versus number of MFDs figure 3-6, it can be seen that with the increase of number of MFDs, the total root mean square error (RMSE) is decreasing. The decrease in RMSE from one MFD to three MFD was rapid but after that the change is very low from scenario of three MFDs to four MFDs. So, three potential macroscopic fundamental diagrams were considered to represent our macroscopic link properties. The first MFD is representing links with higher capacity, indicating urban major roads and highways, the second MFD is representing links with medium capacity, indicating urban minor roads and the last MFD is representing links with low capacity and higher density, indicating urban minor roads suffering from traffic congestion, long queue and spillbacks.

51

Figure 3-6: Determination of Number of MFDs First Macroscopic Fundamental Diagram Link numbers 3, 4, 6, 7, 9-12, 15, 18-19, 23, 28, 32, 35, 37-39, 41-43, 45-48, 51, 54-59, 61, 62, 64, 66, 67, 70, 72-74 are included in the first fundamental diagram, which has higher capacity. The calibrated model equation of volume-density relationship is achieved by using non-linear modeling in JMP. The calibrated equation for the first MFD is given in Table 3-2. The volume-density relationship is shown in Figure 3-7(a).

Second Macroscopic Fundamental Diagram Link numbers 2, 5, 8, 13, 14, 16, 17, 20, 22, 24, 26, 31, 33, 34, 40, 44, 49, 50, 52, 53, 65, 68, 69, 71, 75, 76 are included in the second fundamental diagram, which has lower capacity than the links for the first MFD. The calibrated equation the second MFD is given in Table 3-2. The volume-density relationship is shown in Figure 3-7(b).

Third Macroscopic Fundamental Diagram Link numbers 1, 21, 25, 27, 29, 30, 36, 60, 63 are included in the third category of fundamental diagram, which has lowest capacity due to spillback characteristics. By observing these link behavior, it can be seen that the links are significantly congested after a certain period and spillback is occurred. The calibrated equation for the third MFD is given in Table 3-2, and the volume-density macroscopic fundamental diagram is given in Figure 3-7(c). Table 3-2: Calibrated Equation for base scenario

MFD Calibrated Equation Free flow speed, uf

(mph) Optimal density, k0

(veh/ hr/lane)

First kk

eqln

37.50)77.35ln(

(3.6) 35.77 50.37

130

140

150

160

170

0 1 2 3 4 5

RM

SE

Number of MFDs

Determination of Number of MFDS

52

Second kk

eqln

57.33)39.28ln(

(3.7) 48.16 33.57

Third kk

eqln

68.31)61.35ln(

(3.8) 35.61 31.68

(a)

(b)

(c)

Figure 3-7: Volume-density macroscopic fundamental diagrams in base scenario: (a) 1st MFD; (b) 2nd MFD; and (c) 3rd MFD; (legends: blue to grey to red).

3.9.4 Low and High Volume EV Scenario After modifying the preemption settings in signal timing plan for EV route nodes and inputting EV volume as 18 and 36 vehicle per hour for low and high EV scenario respectively for EV route 1 and 2, it can be observed that the macroscopic properties of links are significantly changed from base scenario in MFDs as well as the link delays. The volume-density relationship for low and high EV flow scenario for both EV route are given in fig. 3-8. The output data from VISSIM are given in table A.7 and table A.8 in Appendix A.


53

(a)

(b)

Figure 3-8: Volume-density fundamental diagram; (a) Low EV; (b) high EV (legends: blue to green to red).

3.9.5 Comparison among Scenarios First Macroscopic Fundamental Diagrams The 1st MFD consists of link number 3, 4, 6, 7, 9-12, 15, 18-19, 23, 28, 32, 35, 37-39, 41-43, 45-48, 51, 54-59, 61, 62, 64, 66, 67, 70, 72-74 in base scenario. The links included in 1st MFD has higher capacity compared to the links of other MFDs. The volume-density relationship in base, low EV and high EV shown in Fig. 3-9(a).

In comparison between base and low EV scenarios in route 1, it is observed that the link points 5, 7, 8, 13, 17, 20, 24, 49, 50, 51, 52, 53, 59, 60, 63, 64, 65, 69, 71, 75, 76 which were in the lower MFDs in base scenario, are now shifted to higher MFD (Fig. 3-9 (b)). The flows in those EV route links were significantly increased due to EVP operation. Beside this, the flow of Link number 9, 10, 11, 17, 18, 23, 28, 35, 37-39, 41, 42, 48, 51, 59, 64, 73 were decreased and shifted to lower MFDs from first MFD.

By increasing EV volume from 18 to 36 vehicle per hour, it can be noticed that change of

link position from lower to 1st MFD MFDs were 2, 8, 10, 16, 18, 21, 24, 25, 29, 30, 31, 37, 42, 49, 50, 63, 75 (Fig. 3-9 (c)). Beside this, the flow of Link number 6, 7, 9, 15, 23, 35, 38, 39, 41, 45-48, 51, 54, 57, 62, 64, 66, 73 were decreased and shifted to lower MFDs from first MFD.

Legends: ascending

order

54

(a)

(b)

(c)

Figure 3-9: Comparison of first MFDs among three scenarios: (a) base; (b) low EV; and (c) high EV (legends: blue to grey to red).

Second Macroscopic Fundamental Diagram In base scenario, the link number 2, 5, 8, 13, 14, 16,17, 20, 22, 24, 26, 31, 33, 34, 40, 44, 49, 50, 52, 53, 65, 68, 69, 71, 75, 76 are included in the second fundamental diagram, which has lower capacity than the links for the first MFD. The volume-density relationship is shown in Fig. 3-10(a).

In comparison between base and low EV scenarios in route 1, it is observed that the link points 5, 7, 8, 13, 17, 20, 24, 49, 50, 51, 52, 53, 59, 64, 65, 69, 71, 75, 76 were shifted from second MFD to higher MFD due to increase of link flows during EVP operation. On the other hand the links 9, 10, 17, 18, 23, 28, 35, 38, 39, 41, 48, 51, 59, 64, 73 which were in the higher MFD in base scenario, are now shifted to second MFD (Fig. 3-10 (b)). The flows in those EV route links were significantly decreased due to EVP operation. Beside this, the flow of Link number 33, 34, 40 were decreased and shifted to lower MFD from second MFD.


link position from higher to second MFD were 6, 7, 9, 15, 23, 35, 38, 39, 41, 45-48, 51, 54, 57, 62, 64, 66, 73 (Fig. 3-10 (c)). It is also observed that the link points 2, 8, 16, 18, 24, 31, 49, 50, 75 were shifted from second MFD to higher MFD

Legends:

ascending order

55

(a)

(b)

(c)

Figure 3-10: Comparison of second MFDs among three scenarios: (a) base; (b) low EV; and (c) high EV (legends: blue to grey to red).

Third Macroscopic Fundamental Diagram The 3rd MFD consists of link number 1, 21, 25, 27, 29, 30, 36, 60, 63 were in base scenario. These links have lowest capacity due to traffic congestion and spillback. By observing these link behavior, it can be seen that the links are significantly congested after a certain period and spillback is occurred. The volume-density macroscopic fundamental diagram is given in Fig. 3-11(a).

In comparison between base and low EV scenarios in route 1, it is observed that the link points 1, 21, 25, 27, 29, 30, 60, 63 were shifted from third MFD to higher MFD due to increase of link flows during EVP operation. On the other hand the links 11, 33, 34, 37, 40, 42 which were in the higher MFD in base scenario, are now shifted to third MFD (Fig. 3-11(b)). The flows in those EV route links were significantly decreased due to EVP operation.


link position from higher to third MFD were 5, 10, 13, 14, 17, 33, 34, 37, 40, 42, 53, 68, 71, 72 (Fig. 20(c)). It is also observed that the link points 1, 21, 25, 27, 29, 30, 60, 63 were shifted from third MFD to higher MFD (Fig. 3-11(c)).

Legends: ascending

order

56

(a)

(b)

(c)

Figure 3-11: Comparison of third MFDs among three scenarios: (a) base; (b) low EV; and (c) high EV (legends: blue to grey to red).

3.10 Evaluation of EVP Operation

3.10.1 Change of link location in MFD From above results and analysis, it can be observed that there is a significant difference in the composition of the MFDs between the base scenario and other two different EVP scenarios in both route. During the EVP operation, the green time for all phase except EV route direction and non-conflicted direction were cut off to clear the intersection for EV. For example, it can be observed on the following node that the green time is increasing in compare with the situations of before check-in and during dwell state (see Table 3-3). After checking out the detector of EV, the signal green time is decreased. Thus, The EVP operation gives a decrease in signal green time of other direction except EV route direction. As a result, the traffic on those links are waiting an extra time to pass through the intersection which creates the link congestion and spillbacks as well as their flow is decreased. Due to this reason, some link positions are shifted from higher to lower MFDs. The jump and drop of link points between MFDs are given in the following table 3-4. Table 3-3: Signal Timing Table during EVP operation

EV Position VISSIM Model

Image Signal Timing Table

Before reaching check-in detector

Legends: ascending

order

57

During passing intersection

After passing check-out detector

Table 3-4: Signal Timing Table during EVP operation

Scenario Jump/Drop

Jump/Drop between MFDs

in compare with base scenario

Link Number

Low

Drop

1st MFD to 2nd MFD

9,10,17,18,23,28,35,38,39,41,48, 51,59,64,73

1st MFD to 3rd MFD

11,37,42,

2nd MFD to 3rd MFD

33,34,40

Jump

2nd MFD to 1st MFD

5,7,8,13,17,20,24,49,50,51,52,53,59,64,65,69,71,75,76

3rd MFD to 2nd MFD

1,21,25,27,29,30

3rd MFD to 1st MFD

60,63

High

Drop

1st MFD to 2nd MFD

6,7,9,15,23,35,38,39,41,45-48,51,54,57,62,64,66,73,

1st MFD to 3rd MFD

10,37,42,72,

2nd MFD to 3rd MFD

5,13,14,17,33,34,40,53,68,71,

Jump

2nd MFD to 1st MFD

2,8,16,18,24,31,49,50,75,

3rd MFD to 2nd MFD

21,25,27,29,30,63,

3rd MFD to 1st MFD

1,60,

58

Figure 3-12: Category of links to describe the effect of EVP on link performance Table 3-5: Signal Timing Table during EVP operation

Drop Jump EV

links Primary Links Secondary Links

EV links

Primary Links

Secondary Links

Base to Low

48 4, 9, 12, 15, 45,

53, 54, 68, 70-72

7, 18, 19, 23, 33-35, 39, 40, 41, 45, 51, 55, 56, 58, 62,

64

- 25,31 24,17,29,3

0,44, 60

Low to High

47-48 67 6,12 - 16,21 63

By comparing between base and low EV scenarios in route 1, it can be observed that some points were shifted among the MFDs such as the location of link number 1, 5, 7, 8, 13, 17, 20, 21, 24, 25, 27, 29, 30, 49, 50, 51, 52, 53, 59, 60, 63, 64, 65, 69, 71, 75, 76 were shifted in lower MFDs from higher MFDs as the flow of these links were decreased due to the EVP operation. Among the links, 49 and 50 are connected to EV nodes. During EVP operation, there were number of green signal interruption of other links except EV link in associated node. That’s how the associated links of To describe the effect of EVP on link performance, three types of links are introduced here which are EV links, primary links and secondary links (see figure 3-12). EV links are associated with EV nodes on EV route. The links which are directly connected with EV nodes are called primary links.

59

The secondary links are those links that are not directly connected to EV nodes. As EV route had a negative interruption in normal traffic flow, there are significant effects on EV link performance. Due to decrease in those links, the traffic flow in link which are connected with them in further approach had also an indirect negative effect such as link number 9-11, 26, 28, 37. This kind of links are secondary victims of EVP operation.

In comparison between base scenarios to low EV volume scenario, it is seen that EV link

48 was dropped from higher to lower MFD as traffic flow was interrupted because the links 48 and its adjacent link 49 have long queues around 559.14 ft. and 422.38 ft. respectively. In high EV volume scenario, situation is getting worse for EV route as links 46 and 47 were also dropped into lower MFDs. This is because of long queues of their links ahead which created traffic congestion. In high EV volume, the location of link points were stable compare to low EV volume scenario. Some link location such as 15, 21, 25, 57, 62, and 73 were shifted to higher MFD from lower MFD. Among them, link 21 and 25 had limited positive effect in increasing flow because of the extra green time due to EVP operation on adjacent EV link 47.

Most of the links which are connected to those nodes along the EV route were affected by

the EVP operation. Also those emergency vehicle route links were resulted in decrease of flow in which traffic congestion and spillback were already present. By comparing the low and high EV volume scenario, it can be observed that the condition of traffic congestion was getting worse by increasing the volume of EV in a large network. In addition, it also can be said that the traffic congestion may be increasing by introducing more than one EV route. This is a very good identification for traffic engineers and practitioners to identify the affected links in a large network due to EVP operation so that they can come up with better optimization strategy to reduce the travel time of EV and the total network delay.

3.10.2 Impact on Total Network Performance There are significant effects on total network performance by introducing EVP operation. By comparing the base and low EV volume scenario in EV route, it can be seen that the total vehicle delay and the total stop delay for EV links are significantly decreased which means the additional green time due to EVP operation along EV links was very helpful to reduce both the vehicle and stop delay on those EV links. It is also observed that the total vehicle delay and total stop delay for normal links are increased significantly. Because the extended green time hinders traffic flows in associated normal links in EV nodes. For this reason, traffic in normal links had to wait for extra time to pass the intersection during EV operation.

In comparison between low and high EV volume scenario, the total vehicle and stop delay in EV route were less than the values in low scenario because introduction of high volume EV cause more extended green time for EV links. This cause decrease in vehicle delay and stop delay. But the change of delays are less between low EV to high EV scenario in compare with the change of delays between base scenario to low EV scenario. This is due to the phenomenon of multiple EV checking in within limited time. During the simulation run in high EV scenario, it was found that two or more EV check in very limited time (see figure 3-13). In figure 3-13, it is seen that two EVs were checked in within short period of time when one EV was about to cross the intersection (EVs indicated in red dot in figure 3-13). When the leading EV passed the check-out detector, the

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green time of phase 6 was cut down without waiting for the other two incoming EVs though they were also checked in a few seconds before. After that the signal phase was changed into another which was phase 4. This is a limitation of preemption settings in VISSIM. Due to this incident, the EVs had to wait until the controllers turns the phase 6 into green or checking in of another EV. In figure 3-13, it can be seen that after couple of seconds, another EV passed the check-in detectors and phase 6 was turned into green again. Because of this phenomenon, EVs have to wait for sometimes which adds vehicle delay and stop delay in EV links. For this reason, the decrease in total vehicle delay and total stop delay were less between low EV and high EV scenarios.

In low EV volume scenario, this phenomenon didn’t occurred because of low EV

penetration rate. But it is sometimes visible in high volume scenario due to high EV penetration rate. On the other hand, the normal links were affected badly due to EVP operation which also shows in total vehicle delay and total stop delay. Both the delays are increased in compare with the values in base scenario. The total vehicle delay and total stop delay for each scenario for both routes are given in the following table 3-6. The individual vehicle delay and stop delay for EV links are given in table 3-7. Table 3-6: Total vehicle delay and total stop delay in various scenarios for both routes

Scenario EV Route Links Normal Links

Total Vehicle Delay (veh-sec)

Total Stop Delay (veh-sec)

Total Vehicle Delay (veh-sec)

Total Stop Delay (veh-sec)

Base 125.18 204.59 7500.21 10586.29 Low EV 78.24 151.03 7717.12 11539.17 High EV 68.57 128.15 7940.79 12051.65

Table 3-7: Comparison of vehicle delay and stop delay of individual EV links between scenarios

EV Links

Vehicle Delay (veh-sec) Stop Delay (veh-sec)

Base Scenario

Low EV Scenario

High EV Scenario

Base Scenario

Low EV Scenario

High EV Scenario

46 15.07 6.81 6.18 23.8 12.63 12.36 47 22.13 10.72 9.87 32.73 17.67 16.67 48 40.02 23.47 16.99 57.92 44.52 28.74 49 4.59 4.18 3.85 14.68 14.06 12.53 50 20.65 12.64 12.6 40.38 32.25 29.71 32 22.72 20.42 19.08 35.08 29.9 27.14

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Figure 3-13: Signal Phase Profile in node 6

3.11 Conclusion and Future Study This study compared various network traffic flow scenarios with and without EVP operation using VISSIM-based Sioux Falls network with the concept of MFD. The MFD can help to understand the important characteristics of a large network. Even during the EVP operation, it can magnify the important problems which can hamper the uniform traffic flow, reduce roadway capacity, create traffic congestion, possibility of accidents and possible spot for bottlenecks, etc. The results discussed above signify the effects of EVP operation on the number and composition of macroscopic fundamental diagrams in a given network. It can be said that well-designed control strategies can reduce traffic congestion and significantly improve capacity of congested links caused by EVP in a large urban network. This can also increase the proper coordination among the intersections and decrease the travel time. All of these can lead the decrease in overall network delay.

Based on the evaluation results of this study, the following conclusions were made: 1. The introduction of EVP operation in an urban network has a positive impact along the EV

route in terms of traffic flow. It is also observed that the volume of EV links along the EV route are improved significantly. During the EVP operation, the green time for all phase except EV route direction and non-conflicted direction were cut off to clear the intersection for EV which gave an extended green time on that EV route phase to clear the EV. It is also visible by observing the total vehicle delay and total stop delay. Both delays in EV route are much lesser than delays in non-preemption scenario.

2. The normal links associated with EV nodes have a negative effect in terms of traffic flow in which the green time are shortened due to provide enough green for EVP operation. Among those links, some of them suffered from decreasing traffic flow with a small scale. Because the green time of EV phase cut off after 4-5 seconds of EV clearing the intersection and the green is switched to next phase. It is also seen that the total vehicle delay and total stop delay are also increased in compare with non-preemption scenario.

3. There is a negative impact in EV links when multiple EVs are checked in within short period of time. After checking out of leading EV, it is observed that the phased along EV links are cut off without preempting following EVs. So, there are some delays for EV waiting. That’s why the decrease in delays of EV links from low EV to high EV scenario are less in compare with the decrease in delays of EV links from base scenario to low EV scenario. For future work, perimeter control and route choice strategy will be introduced into the network

in various frequencies and their effect on macroscopic fundamental diagram will be observed. The impact of multimodal operation on macroscopic diagram is important to understand the characteristics of a large urban network in real situation. The penetration rate of EV has a significant effect on link performance. It is also depends on the types of links, time of day, etc. In

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addition, the route choice prediction models can also make a positive impact in EVP operation for better traffic flow management.

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4 SUMMARY OF FINDINGS AND RECOMMENDATIONS

4.1 Findings Chapter 2 of this thesis focused on the characterization of the performance of different control strategies in urban network with the help of MFDs. First the four renowned single regime models were used for calibration using our dataset. As there are two portion of each MFD – free flow and congested portion. The advantages of deterministic single regime models are their mathematical simplicity and analytical tractability. Deterministic speed-density models are mostly parameter based without consideration of data correlation. Usually free flow speed is relatively easy to estimate from empirical data and mostly lies between speed limit and highway design speed, but jam density is not easy to observe; however, an approximate value of 185-250 veh/mile is a reasonable range. Since optimum density is difficult to observe and varies with highway geometry and environment, a rough estimate of optimum speed is to halve the highway design speed. Though only single regime models were used for the simplicity of analysis, two regime models can also be used for calibration using our dataset and can be compared with our results also.

The results show that there is a significant difference in the composition of the macroscopic fundamental diagram between the base strategy and the modified control strategies. By comparison among the scenarios, it can be noticed that there are less scatter in the modified macroscopic fundamental diagrams. In addition, it can also be noticed that some points are shifted from lower MFD to higher MFD. This means the traffic flow condition of links with spillbacks are improved from traffic congestion situation and the capacity of those links are increased. It also can be said that the green time of those congested links are optimized to allow all traffic to pass the intersections without any unexpected delay due to congestion or spillback. With the change of signal timing plan in congested intersections, the medium or low traffic congestion is reduced and the capacity of those links are increased in such a way so that those links are considered as higher capacity road links. In 3rd modified MFD, there are only four link points where in old 3rd MFD there are nine link points. So, the number of congested links is reduced as well as the capacity of those links are increased.

There is also an impact on origin-destination trips due to the new control strategy. The signal timing changing can reduce the congestion on those links which are suffering from long queues and spillbacks. The control strategy can also reduce the travel time for EV. This also may lead to an improvement in link capacity and a decrease in delay for each O-D trip which leads to improvement of total network traffic flow. The MFD can help to evaluate these characteristics so that the important factors which are related to the uniform traffic flow, roadway capacity, traffic congestion, possibility of accidents and possible spot for bottlenecks are identified. A well-designed control strategies can reduce traffic congestion and significantly improve capacity of congested links in a large urban network with the help of the concept of MFD.

Chapter 3 of this thesis focused on the evaluation of EVP operation in urban network with the concept of MFD. The results show the volume of links along the EV route are improved on those nodes where no traffic congestion or spillbacks were present on base scenario. Most of the

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links which are connected to those nodes along the EV route were badly affected by the EVP operation. Also those emergency vehicle route links were resulted in decrease of flow in which traffic congestion and spillback were already present. By comparing the low and high EV volume scenario, it can be observed that the condition of traffic congestion was getting worse by increasing the volume of EV in a large network. In addition, it also can be said that the traffic congestion may be increasing by introducing more than one EV route. This is a very good identification for traffic engineers and practitioners to identify the affected links in a large network due to EVP operation so that they can come up with better optimization strategy to reduce the travel time of EV and the total network delay.

4.2 Recommendations for Future Research In this study, only vehicular movement is considered in the network model. But in reality, all network consists of multimodal traffic flow which is quite important to consider, so the applicability of these findings to real network would be limited and thus would be recommended as an area for future research. Further research is also recommended in testing the EVP operation with field data in which the concept of multiple EV route, multiple priority strategies, degree of priority cab be introduced to get more realistic results. Also the performance of transit signal priority system can also be evaluated with the MFD in future.

Future research should look into the characterization of urban network on different field by using MFD. Also, discovering ways to evaluating the network performance by MFD on different sectors can ease researchers and practitioners to identify the transportation problems accurately and improve the overall traffic flow condition in urban network.

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75

VISSIM Input & Output data

Table A-1: Distance between Initial and Terminal Nodes

Initial node Terminal node Length

1 2 3.4

1 3 2.4

2 1 3.4

2 6 2.1

3 1 2.4

3 4 1.6

3 12 2.3

4 3 1.6

4 5 1

4 11 1.5

5 4 1

5 6 1.5

5 9 1.1

6 2 2.1

6 5 1.5

6 8 0.9

7 8 1.1

7 18 1.2

8 6 0.9

8 7 1.1

8 9 1.9

8 16 1

9 5 1.1

9 8 1.9

9 10 0.4

10 9 0.4

10 11 0.6

10 15 1.1

10 16 1.1

10 17 1.3

11 4 1.5

11 10 0.6

11 12 2

11 14 0.9

12 3 2.3

12 11 2

12 13 2

13 12 2

13 24 1.5

14 11 0.9

14 15 1.1

14 23 0.9

15 10 1.1

76

15 14 1.1

15 19 2

15 22 1.2

16 8 1

16 10 1.1

16 17 0.4

16 18 0.9

17 10 1.3

17 16 0.4

17 19 0.8

18 7 1.2

18 16 0.9

18 20 2.5

19 15 2

19 17 0.8

19 20 1.1

20 18 2.5

20 19 1.1

20 21 1.3

20 22 1.4

21 20 1.3

21 22 0.6

21 24 1

22 15 1.2

22 20 1.4

22 21 0.6

22 23 1

23 14 0.9

23 22 1

23 24 0.5

24 13 1.5

24 21 1

24 23 0.5

77

Table A-2: Peak Hour Factors for O-D Trips

Destination 1 2 3 4 5 6 7 8 Origin

1 0 1 1 5 2 3 5 8 2 1 0 1 2 1 4 2 4 3 1 1 0 2 1 3 1 2 4 5 2 2 0 5 4 4 7 5 2 1 1 5 0 2 2 5 6 3 4 3 4 2 0 4 8 7 5 2 1 4 2 4 0 10 8 8 4 2 7 5 8 10 0 9 5 2 1 7 8 4 6 8 10 13 6 3 12 10 8 19 16 11 5 2 3 15 5 4 5 8 12 2 1 2 6 2 2 7 6 13 5 3 1 6 2 2 4 6 14 3 1 1 5 1 1 2 4 15 5 1 1 5 2 2 5 6 16 5 4 2 8 5 9 14 22 17 4 2 1 5 2 5 10 14 18 1 0 0 1 0 1 2 3 19 3 1 0 2 1 2 4 7 20 3 1 0 3 1 3 5 9 21 1 0 0 2 1 1 2 4 22 4 1 1 4 2 2 5 5 23 3 0 1 5 1 1 2 3 24 1 0 0 2 0 1 1 2


1 5 13 5 2 5 3 5 5 2 2 6 2 1 3 1 1 4 3 1 3 3 2 1 1 1 2 4 7 12 14 6 6 5 5 8 5 8 10 5 2 2 1 2 5 6 4 8 4 2 2 1 2 9 7 6 19 5 7 4 2 5 14 8 8 16 8 6 6 4 6 22 9 0 28 14 6 6 6 9 14 10 28 0 40 20 19 21 40 44 11 14 39 0 14 10 16 14 14 12 6 20 14 0 13 7 7 7 13 6 19 10 13 0 6 7 6 14 6 21 16 7 6 0 13 7

78

15 10 40 14 7 7 13 0 12 16 14 44 14 7 6 7 12 0 17 9 39 10 6 5 7 15 28 18 2 7 2 2 1 1 2 5 19 4 18 4 3 3 3 8 13 20 6 25 6 5 6 5 11 16 21 3 12 4 3 6 4 8 6 22 7 26 11 7 13 12 26 12 23 5 18 13 7 8 11 10 5 24 2 8 6 5 7 4 4 3


1 4 1 3 3 1 4 3 1

2 2 0 1 1 0 1 0 0

3 1 0 0 0 0 1 1 0

4 5 1 2 3 2 4 5 2

5 2 0 1 1 1 2 1 0

6 5 1 2 3 1 2 1 1

7 10 2 4 5 2 5 2 1

8 14 3 7 9 4 5 3 2

9 9 2 4 6 3 7 5 2

10 39 7 18 25 12 26 18 8

11 10 1 4 6 4 11 13 6

12 6 2 3 4 3 7 7 5

13 5 1 3 6 6 13 8 8

14 7 1 3 5 4 12 11 4

15 15 2 8 11 8 26 10 4

16 28 5 13 16 6 12 5 3

17 0 6 17 17 6 17 6 3

18 6 0 3 4 1 3 1 0

19 17 3 0 12 4 12 3 1

20 17 4 12 0 12 24 7 4

21 6 1 4 12 0 18 7 5

22 17 3 12 24 18 0 21 11

23 6 1 3 7 7 21 0 7

24 3 0 1 4 5 11 7 0

79

Table A-3: Macroscopic link properties using VISSIM data output for base scenario

Link Number

Density, k (veh/mile/lane) Observed Volume, v (veh/hr)

1 0.6 23.55

1 1.47 53.92

1 2.86 110.25

1 0 0

1 6.49 143.11

1 8.28 64.39

1 35.32 152.9

2 6.05 295

2 0.71 28.43

2 6.87 331.95

2 8.61 38.67

2 12.53 469.9

2 13.86 578.55

2 14.18 363.91

2 25.29 685.22

2 22.11 1002

2 18.3 858.08

2 15.68 760.07

2 14.28 685.42

3 4.51 218

3 4.12 171.39

3 2.95 144

3 2.73 126.36

3 2.09 87.21

4 12.23 236.93

4 11.15 123.41

4 2.72 129.96

4 3.83 184.28

4 0.02 1

4 4.9 236

5 0.12 6

5 1.26 62

5 2.88 139.98

5 0.67 32.09

5 3.89 145

6 2.44 84.97

6 1.33 48.69

6 2.06 73.82

7 12.51 610.12

80

7 5.29 341.92

7 36.9 1268.64

7 31.09 1059.07

7 27.08 896.88

7 28.84 744.96

7 55.04 640.37

7 106.87 539.16

7 133.75 428.97

7 1.7 5.18

7 23.29 491.1

7 43.15 483.83

7 95.06 433.45

7 156.24 344.65

8 262.09 79.29

8 152.98 185.58

8 238.85 130.25

8 218.72 108.47

8 232.36 189.9

8 204.95 177.06

8 89.1 69.71

9 1.99 97

9 3.13 151.15

9 0.69 34

9 3.94 187.87

9 0.05 2

10 0.86 42

10 0.51 25.19

10 0 0

11 10.06 302

11 14.2 301.89

11 0.28 10.42

11 7.47 223.39

12 9.38 339

12 3.23 118.63

12 0.08 3

12 1.12 38

13 6.71 217.71

13 9.94 369

13 8.33 261.17

14 5.85 264.45

14 8.81 431

14 1.59 64.09

14 3.82 187.31

81

14 12.36 531.23

15 18.53 656.44

15 10.6 499.38

15 49.32 496.07

15 154.38 495.54

15 1.37 50.7

15 7.85 300.56

15 9.26 451

15 46.89 463.56

15 107.18 452.7

15 158.6 439.27

15 220.31 403.44

16 9.18 440

16 39.65 455.17

16 53.23 465

16 79.05 469.07

17 0.59 29.57

17 0 0

17 0.96 56

18 0.41 20

18 10.34 9.06

19 2.67 119.15

19 2.5 116.16

19 4.13 172.82

19 1.62 80

19 0.52 24.22

20 5.78 280

20 0.23 11.2

20 4.65 225.88

20 2.61 126.14

21 47.44 68.52

21 80.43 76.31

21 112.9 80.62

21 139.03 88.12

21 198.55 114.32

21 59.56 321.21

21 98.76 242.32

21 138.85 203.64

21 163.08 126.66

21 12.13 13.34

21 12.97 490

22 10.72 362.79

22 2.99 123.48

82

22 2.15 79.06

22 4.88 213

22 7.51 294.41

22 0 0

23 1.34 66

23 0.14 7

23 2.68 131

24 2.4 104.9

24 0.51 24.63

24 4.47 214

24 6.9 325.11

24 23.43 204.39

24 26.53 344.51

24 50.58 349.4

25 4.36 208.96

25 32.48 178.32

25 78.42 148.68

25 180.25 147.96

25 130.27 113.86

25 0.42 21

25 32.49 55.79

25 57.06 60.63

25 122.94 59.52

25 145.35 51.88

26 2.51 115.47

26 5.17 238.18

26 10.83 472

26 8.13 353

27 24.75 317.63

27 39.91 309.52

27 54.61 307.95

27 9.54 357

27 8.98 296.86

28 4.54 155.96

28 9.41 359

28 47.23 364

28 103.55 366

28 147.3 353.61

28 168.97 352.11

28 0.8 30.57

28 0.26 7.39

29 5.22 194

29 32.07 184.99

83

29 67.74 120.4

29 147.68 17.46

30 4.59 173.13

30 0.12 6

30 2.6 98

31 4.5 150.58

31 2.68 100.09

31 2.04 100

32 4.15 154.12

32 4.19 204

32 2.68 121.38

33 9.16 524

33 18.15 521

33 27.16 521

34 2.78 98

34 0.67 52

34 0.19 15

35 5.6 245

35 3.65 164.62

35 0.21 17

36 47.43 523.39

36 68.06 482.11

36 109.77 376.29

36 147.81 287.39

36 3.37 7.07

36 25.54 119.24

36 65.02 114.13

36 122.81 121.32

36 172.78 103.32

36 212.54 217.6

36 195.98 137.15

36 254.5 118.3

37 124.53 495.75

37 147.85 488.38

37 166.95 459.5

37 247.36 372.96

37 179.43 434.43

37 202.37 402.78

37 226.78 376.47

38 6.52 247

38 9.99 247

39 2.16 83

39 0.61 23.46

84

40 70.5 333.48

40 103.05 294.97

40 166.45 210.5

40 189.03 181.01

40 234.15 182.64

40 39.59 354.31

40 179.54 187.15

41 208.57 197.4

41 239.94 230.08

41 169.93 226.32

41 246.49 143.34

41 221.12 133.6

41 246.8 76.86

41 232.46 69.25

42 1.5 73

42 0.41 20

42 2.71 130

43 2.37 89.8

43 3.99 151

43 0 0

44 2.88 79.63

44 3.47 170

44 7.48 254.85

45 0.57 22.4

45 3.88 192.1

45 8.97 345.11

45 11.02 450.6

45 6.72 291.4

46 6.41 303.36

46 5.25 230.18

46 2.26 62.19

47 3.12 91.24

47 3.55 172.96

47 5.87 266.98

48 3.45 154

48 13.24 122.11

48 7.21 343

48 13.01 568.68

48 32.15 595.85

49 8 381.15

49 3.09 147.01

49 4.71 76.45

49 14.17 645.11

85

49 31.64 710.55

50 3.37 103.72

50 7.22 311.33

50 3.81 186

51 1.24 55

51 0.06 5

52 1.37 82

52 1.03 81

52 1.48 109

52 1.82 109

52 0 0

52 0.29 16.02

52 0.43 31

53 7.28 313.91

53 0 0

53 3.46 148.94

53 27.63 257.86

53 75.47 396.82

53 15.86 670.75

53 13.78 503.25

53 23.4 459.41

53 49.24 368.92

53 65.04 298.18

53 90.36 301.03

54 2.46 80

55 3.18 122

56 2.13 97

56 3.11 163

56 0.08 6

57 1.11 88

57 7.41 450.59

57 3.14 244.86

57 0.57 44

58 0 0

59 64.96 383.51

59 91.94 377.07

59 124.42 309.76

59 116.64 294.59

59 145.77 263.46

59 180.38 203.54

59 221.64 195.41

59 250.48 192.08

60 15.79 383.67

86

60 43.97 330.51

60 64.85 322.92

60 86.77 314.3

61 16.69 682.99

61 45.63 676

61 94.07 670.46

61 153.91 662.61

62 0.98 43

62 1.78 87

63 29.38 226.3

63 95.19 216.21

63 114.61 162.32

63 172.07 159.87

63 224.24 146.4

63 0.05 1.02

63 4.52 203.31

63 169.32 218.7

63 222.57 223.7

64 20.52 760

64 19.19 470.98

64 76.09 431.27

64 179.45 421

64 1.77 70.04

64 12.3 304.83

64 64.61 332.68

64 128.18 334

64 226.51 312.41

65 6.05 277.09

65 2.13 94.83

65 9.41 450.56

65 12.02 569

65 56.87 562

66 2.89 93.08

66 9.63 340.78

66 4.79 227.8

67 2.97 143

67 1.74 85

67 7.44 120.74

67 0.06 3

68 4.51 156.15

68 6.44 278.62

68 1.26 57.95

69 8.84 301.61

87

69 4.86 190.93

69 0.79 25.24

69 11.74 442.85

70 36.56 293.81

70 85.44 288.44

70 142.92 247.05

70 183.48 228.61

70 217.61 216.5

70 7.34 283.75

70 171.2 308.76

70 138.3 304.22

71 6.12 299

71 2.7 86.14

71 12.41 509.18

71 46.76 572.94

72 5.2 208.93

72 3.04 147

73 0.26 21

74 5.36 295.05

74 7.96 433.69

74 2.4 184.21

75 10.89 495.09

75 57.1 511.21

75 6.38 312

75 20 93.25

75 92.37 381.95

75 143.29 291.76

75 53.59 80.24

75 78.68 200.21

75 114.84 232.21

75 169.62 249.11

75 234.39 193.93

76 2.2 83.67

76 6.77 258

76 0.69 23.95

88

Table A-4: Macroscopic link properties for WOCL scenario

Link Number Density, k (veh/mile/lane) Observed Volume, v (veh/hr)

1 0.67 26.43 1 1.55 58 1 3.02 117.05 1 0 0 1 6.69 147.58 1 8.44 65.24 1 33.6 148.36 2 6.09 297 2 0.72 28.25 2 6.47 312.14 2 6.59 30.07 2 12.48 467.67 2 13.84 576.26 2 15.56 362.44 2 28.82 687.79 2 21.86 989 2 17.92 839.9 2 15.41 747 2 14.24 682.56 3 4.3 208 3 4.03 169.13 3 2.92 143 3 2.66 123.39 3 1.99 83.72 4 9.79 235.8 4 6.04 120.79 4 2.66 127.18 4 3.78 182.71 4 0.08 4 4 4.84 234 5 0.23 10.96 5 1.36 67 5 2.92 141.18 5 0.68 32.53 5 3.83 143 6 2.25 80 6 1.43 49.39 6 1.78 64.16 7 12.63 605.8

89

7 5.28 348.12 7 33.44 1258.78 7 31.35 1046.57 7 26.39 886.56 7 27.46 714.82 7 73.76 609.38 7 110.39 506.34 7 142.54 412.03 7 2.57 6.72 7 27.17 508.35 7 58.72 496.5 7 112.1 450.31 7 161.35 350.68 8 247.83 73.68 8 197.81 189.83 8 246.14 138.75 8 210.26 111.3 8 216.22 188.02 8 228.92 177.5 8 93.08 69.5 9 2.07 101 9 3.21 155.04 9 0.7 34 9 3.99 190.21 9 0.01 0.38 10 0.71 35 10 0.4 19.36 10 0 0 11 10.64 319 11 14.34 318.44 11 0.29 11 11 7.76 231.56 12 9.78 355.59 12 3.36 124.14 12 0 0 12 1.06 36 13 6.7 214.58 13 9.6 360 13 8.18 258.09 14 6.01 269.99 14 8.72 426 14 1.54 61.85 14 3.73 182.9

90

14 12.53 533.79 15 18.44 651.16 15 10.6 512.83 15 10.48 506.15 15 11.01 501.78 15 1.45 54.41 15 8.22 319.97 15 9.25 449.21 15 9.29 450.85 15 9.29 449.98 15 9.85 450.99 15 137.62 396.07 16 10.39 498 16 21.43 500 16 67.82 510.25 16 80.36 513.64 17 0.61 30.37 17 0 0 17 0.88 51 18 0.34 17 18 10.36 8.89 19 2.6 117.27 19 2.5 117 19 4.13 175.49 19 1.69 83 19 0.57 26.66 20 5.66 274 20 0.24 11.65 20 4.56 221.04 20 2.58 124.77 21 38.78 63.22 21 51.6 82.04 21 109.19 86.8 21 111.73 93.97 21 189.48 103.37 21 57.74 337.57 21 88.27 271.95 21 102.79 237.28 21 148.53 154.84 21 16.2 16.88 21 12.88 483.25 22 10.73 357.66 22 3.07 126.11

91

22 2.04 74.94 22 4.74 207 22 7.32 285.8 22 0 0 23 1.25 61 23 0.2 10 23 2.79 136.84 24 2.4 104.12 24 0.45 21.56 24 4.47 213.99 24 6.61 322.05 24 5.22 182.31 24 8.26 358.55 24 10.72 366.69 25 4.5 215.53 25 45.75 179.43 25 100.66 165.35 25 150 153.55 25 152.2 118.19 25 0.37 18 25 50.88 57.3 25 80.35 55.88 25 117.08 61.29 25 157.74 61.3 26 2.38 109.47 26 4.85 223.79 26 11.08 483.68 26 8.04 349 27 41.88 350.17 27 57.35 341.51 27 57.73 339.74 27 10.6 395 27 10.01 327.77 28 4.43 152.45 28 9.1 348 28 20.26 346 28 71.38 344 28 140.78 347.92 28 153.61 348.23 28 0.74 28.68 28 0.2 6.07 29 5.02 190 29 9.78 189.55

92

29 126.99 125.89 29 297.21 23.24 30 4.33 162.98 30 0.08 4 30 2.62 98 31 4.24 142.73 31 4.13 144.6 31 2.28 111 32 4.13 152.5 32 4.08 198 32 2.69 121.98 33 9.15 523 33 15.33 517 33 22.78 517 34 2.78 98 34 0.67 52 34 0.19 15 35 5.43 238 35 3.69 167.27 35 0.21 17 36 51.74 532.91 36 66.52 478.72 36 91.97 372.26 36 170.95 294.41 36 21.29 8.68 36 42.14 119.28 36 59.96 144.17 36 114.15 136.2 36 130.57 125.44 36 204.91 233.9 36 169.73 148.97 36 237.36 128.96 37 97.18 521.15 37 128.92 513.27 37 167.58 473.37 37 229.39 399.41 37 183.31 451.18 37 200.16 413.52 37 211.35 394.62 38 6.69 252 38 9.14 252 39 2.19 83.77 39 0.61 23.46

93

40 82.12 330.35 40 104.19 286.3 40 177.53 199.67 40 204.09 177.38 40 217.25 172.1 40 55.75 353.94 40 187.21 186.25 41 239.46 191.35 41 224.88 233.35 41 213.98 241.46 41 198.48 184.55 41 219.58 163.9 41 233.88 94.53 41 251.05 78.66 42 1.56 76 42 0.41 20 42 2.87 135 43 2.38 90.37 43 4.03 152 43 0 0 44 2.97 81.91 44 3.22 157 44 7.33 250.54 45 0.55 21.72 45 3.81 188.1 45 9.04 348.02 45 11.12 455.28 45 6.87 298.23 46 6.24 294.41 46 5.11 227.9 46 2.27 62.49 47 3.22 95.37 47 3.78 184.34 47 5.86 268.82 48 3.12 144.99 48 16.9 100.05 48 7.39 355.48 48 12.6 578.9 48 27.35 616.42 49 7.77 370.9 49 3.05 140.16 49 10.73 95.13 49 14.88 619.34

94

49 32.58 661.87 50 3.24 99.74 50 7.56 316.99 50 3.45 168.28 51 1.12 50 51 0.08 6 52 1.32 80 52 1 79 52 1.44 106 52 1.79 107 52 0 0 52 0.21 11.99 52 0.45 33 53 7.86 364.62 53 0 0 53 3.4 161.33 53 37.89 354 53 93.74 405.05 53 14.66 696.55 53 16.34 466.94 53 19.47 398.08 53 55.14 335.55 53 67.76 312.5 53 96.4 305.14 54 2.46 80 55 3.26 124 56 2.19 98.7 56 3.07 161 56 0.13 10 57 1.03 81 57 7.43 451.06 57 3.11 243 57 0.6 45.56 58 0 0 59 90.64 387.11 59 103.12 380.39 59 92.74 306.57 59 119.46 292.47 59 153.81 257 59 196.46 208.55 59 233.01 200.93 59 256.09 204.22 60 10.35 378

95

60 58.93 327.74 60 40.63 319.06 60 87.6 312.83 61 16.26 666 61 62.71 665 61 112.32 663.1 61 155.77 661.85 62 0.99 43.15 62 1.74 85 63 20.47 230.39 63 72.47 229.74 63 165.93 150.97 63 179.39 147.56 63 220.8 130.52 63 0.08 1.52 63 4.62 210.46 63 187.43 229.95 63 214.91 233.97 64 21.7 803 64 24.88 498.83 64 80.26 451.02 64 201.44 447.64 64 2.04 80.7 64 16.86 326 64 81.42 355.23 64 145.46 354.77 64 233.15 348.58 65 6.77 314.65 65 2.48 112.06 65 10.4 498.45 65 13.37 634 65 86.37 639 66 3.56 114.92 66 11.04 383.95 66 5.54 261.34 67 2.88 138 67 1.71 83 67 8.28 123.92 67 0.12 6 68 4.29 151.45 68 6.37 274.69 68 1.08 49.81 69 8.58 290.87

96

69 4.1 163.93 69 0.65 20.9 69 11.51 437.2 70 102.38 275.65 70 137.04 248.35 70 194.43 227.2 70 192.81 207.88 70 220.53 212 70 54.34 307.84 70 177.77 339.56 70 143.04 317.95 71 6.58 319.77 71 9.08 117.77 71 14.09 516.32 71 78.36 595.77 72 5.74 236.54 72 3.62 175 73 0.36 29 74 5.63 306.94 74 7.98 432.95 74 2.35 183.12 75 57.99 438.97 75 76.85 451.31 75 6.04 293 75 71.47 79.44 75 101.17 359.41 75 134.09 245.4 75 54.66 62.21 75 88.5 127.24 75 116.35 178.88 75 147.62 214.27 75 213.08 159.48 76 2.07 78.9 76 6.5 248 76 0.67 23.12

97

Table A-5: Macroscopic link properties for WOCL+10sec scenario


1 0.64 24.95

1 1.48 55

1 2.95 113.91

1 0 0

1 6.19 136.02

1 8.04 61.76

1 34.91 156.73

2 5.76 281

2 0.71 29.01

2 6.97 335.57

2 7.11 30.41

2 12.57 469.12

2 13.99 580.18

2 10.84 358.84

2 19.46 706.01

2 21.82 985.7

2 17.59 824.81

2 15.52 752.76

2 14.41 692.1

3 4.76 230

3 4 164.33

3 2.64 129

3 2.72 126.39

3 2.24 93.64

4 15.97 232.9

4 29.02 120.79

4 2.67 127.74

4 3.75 181.15

4 0.04 2

4 4.8 232

5 0.16 7.64

5 1.31 64

5 2.79 135.3

5 0.66 31.64

5 3.98 148

6 2.27 81.9

6 1.4 51.85

6 1.83 65.25

7 20.08 585.85

7 11.76 352.61

98

7 49.45 1179.05

7 54.36 982.43

7 46.68 843

7 47.37 712.18

7 71.56 613.51

7 100.63 512.11

7 154.24 409.24

7 8.77 15.91

7 38.32 444.98

7 61.15 443.85

7 109.95 419.72

7 145.7 343.48

8 238.07 64.46

8 184.61 181.51

8 222.29 132.1

8 232.26 110.44

8 217.57 185.17

8 260.62 170.31

8 89.69 68.43

9 1.82 89

9 3.09 150

9 0.9 44

9 4.17 199.95

9 0 0.02

10 0.82 40

10 0.45 22.08

10 0 0

11 9.36 280

11 15.31 279.67

11 0.26 9.66

11 6.94 206.47

12 9.21 333

12 3.14 115.33

12 0.03 1

12 1.03 35

13 5.65 182.64

13 8.42 317

13 6.92 220.93

14 6.29 283.33

14 8.88 432.98

14 1.53 61.1

14 3.68 179.85

14 12.24 525.49

99

15 19.08 684.64

15 11.34 547.89

15 11.97 545.52

15 41.5 521.67

15 1.46 54.56

15 8.43 324.36

15 9.54 463

15 10.51 476.96

15 15.44 478.09

15 40.12 469.55

15 221.56 444.87

16 10.58 507

16 18.6 507

16 52.31 507.69

16 78.05 515.37

17 0.61 30.74

17 0 0

17 1.04 61

18 0.41 20

18 12.92 9.1

19 2.66 119.41

19 2.52 118

19 4.18 181

19 1.67 82

19 0.48 23.07

20 5.52 267

20 0.2 10

20 4.45 215.39

20 2.52 121.56

21 39.43 66.88

21 52.18 73.74

21 100.36 90.19

21 127.42 89.74

21 160.32 120.08

21 45 331.68

21 76.93 256.58

21 110.86 213.91

21 170.12 134.09

21 6.24 15.31

21 12.97 487

22 10.61 359.21

22 2.77 114.55

22 1.94 71.76

100

22 4.57 200

22 7.57 291.86

22 0 0

23 1.32 65

23 0.16 8

23 2.77 135

24 2.19 95.08

24 0.35 16.76

24 4.69 225

24 8.04 351.54

24 76.87 241.63

24 58.67 297.86

24 131.49 300.74

25 4.41 215

25 16.24 195.23

25 81.43 158.98

25 156.44 173.32

25 115.62 126.66

25 0.24 12

25 19.54 45.61

25 68.08 45.03

25 90.59 53.78

25 140.61 45.04

26 2.42 111.67

26 4.99 230.46

26 10.99 478

26 7.78 337

27 61.43 316.71

27 72.13 307.75

27 74.16 306.14

27 10.99 373.46

27 18.61 298.11

28 4.49 155.52

28 9.26 353.66

28 9.28 352

28 9.55 350

28 42.34 350

28 90.19 350

28 0.52 20

28 0.16 4.75

29 10.67 205.6

29 52.07 193.3

29 72.81 128.52

101

29 294.85 26.98

30 4.38 163.97

30 0.08 4

30 2.5 94

31 4.32 146.39

31 3.69 136.62

31 2.25 110

32 4.27 157.2

32 4.21 205

32 2.77 124.38

33 9.05 517

33 16.07 517

33 24.11 517

34 2.78 98

34 0.68 52

34 0.2 16

35 5.48 240

35 3.8 171.45

35 0.23 18

36 33.14 548.8

36 63.53 483.08

36 119.95 372.65

36 132.87 284.09

36 0.35 2.35

36 36.18 86.7

36 50.99 118.36

36 146.85 107.79

36 170.7 100.15

36 191.72 218.56

36 198.75 144.18

36 240.33 138.21

37 145.18 489.69

37 172.41 483.55

37 180.37 455.87

37 242.13 371.1

37 182.54 431.43

37 198.02 394.94

37 214.56 371.7

38 5.89 224

38 7.56 225

39 2.19 84

39 0.62 23.74

40 84.6 345.88

102

40 103.28 299.74

40 172.61 211.11

40 185.81 186.49

40 235.16 167.18

40 52.18 352.38

40 177.74 174.1

41 224.61 210.14

41 256.43 238.29

41 164.35 235.83

41 241.01 142.05

41 218.62 133.56

41 228.04 88.86

41 248.59 74.22

42 1.72 84

42 0.33 15.88

42 2.94 142

43 2.3 87.51

43 3.86 146

43 0 0

44 2.32 63.76

44 3.18 156

44 7.38 253.61

45 0.52 20.5

45 3.67 181.1

45 8.97 345.84

45 11.06 453.5

45 7.01 304.76

46 6.12 290

46 5.18 227.82

46 2.19 60.21

47 2.99 87.67

47 3.59 175

47 5.79 263.68

48 3.56 162.72

48 14.78 123.83

48 7.36 350.49

48 12.17 562.86

48 24.08 605.8

49 7.91 377.9

49 2.83 134.19

49 5.78 78.99

49 14.4 660.3

49 30.2 712.21

103

50 3.05 96.03

50 7.14 311.01

50 4 195

51 1.2 53

51 0.06 5

52 1.34 81

52 1.02 80

52 1.51 111

52 1.86 111

52 0 0

52 0.28 14.08

52 0.46 33

53 7.88 366.01

53 0 0

53 2.79 139.36

53 17.46 350.94

53 96.43 419.33

53 14.33 682.42

53 10.64 490.58

53 9.65 400.23

53 19.81 340.89

53 39.82 282.44

53 89.16 278.2

54 2.46 80

55 3.19 121

56 2.2 100

56 3.1 161

56 0.09 7

57 0.99 78

57 7.3 442.44

57 3.18 247

57 0.59 46

58 0 0

59 109.5 342.99

59 111.06 338.67

59 124.66 276.58

59 157.21 265.15

59 179.17 233.18

59 236.64 181.05

59 236.87 172.68

59 235.59 172.14

60 19.1 363.63

60 76.77 308.29

104

60 79.43 299.69

60 85.14 292.41

61 15.39 624.03

61 67.84 613.63

61 84.07 605.27

61 136.96 599.57

62 0.75 33.44

62 1.67 82

63 26.82 215.34

63 48.33 216.24

63 190.61 158.82

63 201.43 156.65

63 259.45 148.05

63 0.09 1.52

63 4.69 211.74

63 220.69 206.14

63 252.85 207.65

64 21.65 801

64 17.15 512.63

64 43.39 457.48

64 160.66 434.56

64 2 79.4

64 10.69 294.59

64 44.77 332.94

64 93.88 337.01

64 219.88 326.18

65 6.4 296.2

65 2.36 104.01

65 9.97 477.47

65 12.75 600

65 59.47 609

66 3.65 114.93

66 11.24 401

66 5.84 277

67 2.87 138

67 1.75 85

67 7.63 124.69

67 0.12 6

68 4.41 153.9

68 6.6 284.78

68 1.13 51.76

69 8.62 293

69 4.51 183.44

105

69 0.79 25.36

69 11.85 443.93

70 8.6 294.27

70 51.09 264.92

70 138.51 240.86

70 178.15 211.49

70 216.05 218.17

70 6.61 276.14

70 157.81 354.1

70 170.37 323.42

71 6.23 303

71 2.99 93.78

71 12.88 510.34

71 76.88 582.54

72 6.05 250.58

72 3.34 162

73 0.34 27

74 5.11 278.45

74 7.77 423

74 2.53 194.85

75 48.3 431.36

75 84.38 452.08

75 6.43 314.09

75 50.05 85.15

75 137.52 331.46

75 155.32 253.18

75 72.81 102.68

75 124.55 164.7

75 144.01 192.57

75 180.92 232.5

75 209.81 171.23

76 2.21 83.99

76 6.75 257

76 0.69 23.86

106

Table A-6: Macroscopic link properties for WOCL-10sec scenario


1 0.65 26.43

1 1.54 58

1 2.93 113.94

1 0 0

1 6.46 143.07

1 7.79 60.85

1 35.19 158.07

2 6.34 309

2 0.75 29.66

2 6.89 331.97

2 8.01 40.23

2 12.88 471.96

2 13.75 557.98

2 7 339

2 15.5 723.59

2 21.85 996

2 18.07 850.1

2 15.77 763.81

2 14.85 713.91

3 4.93 238

3 4.33 177.93

3 2.66 130

3 2.62 121.57

3 2.06 86.1

4 10.06 241.87

4 7.92 124.99

4 2.75 131.62

4 3.93 189.74

4 0.02 1

4 5.04 243

5 0.18 9

5 1.4 69

5 2.87 140

5 0.67 32.53

5 3.9 146

6 2.29 83

6 1.52 55.04

6 1.71 62.51

7 12.67 609.23

7 5.72 367.31

7 39.95 1235.2

7 40.2 1024.05

7 36.63 866.9

7 38.09 727.88

107

7 70.46 614.35

7 98.06 522.35

7 151.95 422.37

7 3.79 4.29

7 27.02 472.76

7 55.35 480.97

7 105.2 423.26

7 147.9 340.95

8 235.99 85.34

8 183.65 192.29

8 209.99 138.87

8 178.36 123.6

8 209.67 154.05

8 194.35 151.77

8 83.76 70.25

9 2.14 104

9 3.19 154.37

9 0.67 33

9 4.14 197.74

9 0.07 3.05

10 0.94 46

10 0.44 21.74

10 0 0

11 10.17 299

11 17.38 298.55

11 0.28 10.55

11 7.27 220.56

12 9.34 339

12 3.22 118.63

12 0 0

12 0.99 34

13 6.13 197.52

13 8.79 328

13 7.26 233.66

14 6.47 286.4

14 9.16 445.81

14 1.46 57.84

14 3.97 194.65

14 12.83 551.08

15 19.45 700.67

15 11.38 549.2

15 11.39 549

15 13.03 535.53

15 1.47 55.01

15 8.52 329.33

15 9.76 474.29

15 9.91 481

108

15 9.99 481.95

15 12.42 496

15 152 475.93

16 10.18 488

16 19.07 488

16 56.71 498.9

16 85.38 504.14

17 0.51 25.51

17 0 0

17 0.98 57

18 0.41 20

18 5.06 7.87

19 2.79 124.7

19 2.63 123

19 3.97 169

19 1.47 72

19 0.55 25.31

20 5.6 272

20 0.1 5.2

20 4.52 219.43

20 2.57 124.77

21 29.82 76.56

21 57.84 86.97

21 113.38 89.56

21 132.55 96.76

21 185.33 110.46

21 68.63 317.26

21 109.16 252.48

21 125.05 207.15

21 151.07 132.49

21 13.55 15.31

21 13.05 490

22 10.53 355.7

22 3.02 124.28

22 2.17 80.12

22 4.82 211

22 7.47 291.16

22 0 0

23 1.38 68

23 0.1 5

23 2.76 135

24 2.28 99.79

24 0.36 17.25

24 4.28 204

24 7.05 330.44

24 46.21 218.23

24 15.21 314.43

109

24 72.33 318.15

25 4.42 214.57

25 35.28 196.76

25 75.96 162.14

25 161.23 140.42

25 125.14 119.57

25 0.2 10

25 33 34.26

25 55.34 39.79

25 85.39 45.45

25 156.67 46.97

26 2.48 114.92

26 5.04 234.24

26 10.83 473

26 8.02 349

27 41.76 333.2

27 56.82 323.11

27 45.11 321.47

27 9.91 369

27 8.49 307.96

28 4.3 148.49

28 8.81 337

28 8.79 333

28 12.74 333

28 123.38 339.33

28 142.42 340.82

28 0.61 23.53

28 0.18 5.38

29 12.69 194.55

29 23.24 180.38

29 145.44 116.86

29 149.05 12.53

30 4.72 177.41

30 0.1 5

30 2.75 103.27

31 4.44 144.91

31 3.1 115

31 2.13 104

32 4.36 158.35

32 4.26 209

32 2.74 123.78

33 9.02 515

33 22.79 523

33 40.23 524

34 2.78 98

34 0.67 52

34 0.19 15

110

35 5.42 238

35 3.69 167

35 0.21 17

36 35.76 553.65

36 56.78 497.35

36 67.88 371.4

36 130.43 281.07

36 12.79 6.34

36 22.15 104.1

36 54.83 124.87

36 125.95 110.94

36 150.89 100.88

36 180.85 243.83

36 176.33 161.03

36 244.47 147.82

37 134.17 492.55

37 154.46 485.39

37 175.52 451.73

37 233.09 387.8

37 173.98 434.07

37 214.61 393.82

37 221.48 382.63

38 6.39 241

38 8.29 241

39 2.25 86

39 0.64 24.31

40 76.18 322.84

40 105.68 286.16

40 178.85 208.31

40 197.52 189.31

40 240.07 181.92

40 57.63 342.65

40 163.79 178.83

41 223.5 194.32

41 253.59 222.5

41 205.21 222.04

41 214.54 157.65

41 229.11 143.34

41 220.78 86.55

41 234.66 74.48

42 1.5 73

42 0.37 17.94

42 2.64 127

43 2.23 84.65

43 3.75 142

43 0 0

44 2.32 62.87

111

44 2.97 144.68

44 7.27 249.8

45 0.56 21.93

45 4.03 199.1

45 9.03 348.02

45 11.13 455.07

45 6.69 289.99

46 6.06 286.66

46 5.07 223.22

46 2.24 60.9

47 3.14 92.33

47 3.46 168

47 6.13 281.36

48 3.98 174.66

48 23.29 127.84

48 7.47 357.29

48 12.49 557.98

48 33.71 588.63

49 7.78 373

49 2.95 140.88

49 4.19 75.91

49 13.04 598.15

49 24.41 658.09

50 3.14 98.26

50 7.33 313.57

50 3.7 180.18

51 1.19 53

51 0.08 6

52 1.36 81

52 1 79

52 1.51 111

52 1.88 112

52 0 0

52 0.26 15.24

52 0.48 35

53 7.4 344.16

53 0 0

53 2.65 131.69

53 5.52 224.87

53 54.47 357.84

53 14.29 677.99

53 11.28 536.49

53 11.08 505.78

53 10.29 427.84

53 12.96 300.02

53 18.41 299.82

54 2.46 80

112

55 3.3 126

56 2.28 106

56 3.16 166.03

56 0.1 8

57 1.14 90

57 7.43 450.35

57 3.11 242

57 0.64 47.58

58 0 0

59 101.07 388.51

59 79.35 382.72

59 121.6 314.31

59 131.69 298.99

59 169.04 265.46

59 198.33 212.71

59 228.21 207.96

59 255.63 206.11

60 21.66 391.98

60 64.47 335.05

60 32.7 327.95

60 98.1 322.77

61 15.62 633

61 28.02 628

61 63.01 627

61 116.41 625.85

62 1.08 47.34

62 1.69 83

63 56.42 220.6

63 97.61 222.54

63 166.85 167.65

63 185.08 164.57

63 226.63 147.07

63 0.14 3.05

63 4.78 205.27

63 211.01 206.88

63 226.69 213.59

64 20.61 765

64 16.82 512.5

64 43.19 442.31

64 143.19 424.65

64 1.95 76.88

64 9.76 278.69

64 33.07 323.19

64 80.13 333.69

64 218.8 317.15

65 6.15 283.08

65 2.43 111.92

113

65 9.44 453.98

65 12.4 582

65 45.01 587

66 3.29 105.06

66 10.4 370.04

66 5.2 247

67 2.94 141

67 1.82 89

67 8.12 131.63

67 0.02 1

68 4.18 143.78

68 6.19 263.08

68 1.13 52.47

69 8.62 292.76

69 4.72 182.69

69 0.75 23.78

69 11.45 429.7

70 109.73 251.2

70 182.66 233.2

70 195.87 230.09

70 211.23 222.1

70 233.54 222.39

70 51.01 301.51

70 182.17 305.91

70 182.25 293.21

71 6.28 306

71 2.66 88.88

71 13.48 521.14

71 57.73 576.34

72 5.46 224.45

72 3.43 167

73 0.29 23

74 5.39 293

74 8.11 439.73

74 2.39 186

75 46.4 459.53

75 63.75 474.95

75 6.21 303

75 49.85 93.65

75 108.55 360.47

75 151.15 256.16

75 75.76 92.65

75 113.32 179.81

75 144.03 229.74

75 183.89 249.19

75 208.12 180.48

76 2.19 83.67

114

76 6.57 251

76 0.68 23.3

Table A-7: Macroscopic Link Properties for Low EV scenario


1 0.67 26.34

1 1.51 57

1 2.99 116.97

1 13.22 515

1 3.11 119

1 3.1 119

1 3.19 122

2 3.19 122

2 3.19 122

2 3.19 121.75

2 3.17 121

2 3.17 121

2 3.17 121

2 0.93 41

2 0.94 41

2 0.2 16

2 0.2 16

2 0.2 16

2 2.15 97.67

3 0.39 17

3 0.39 17

3 0.52 23

3 0.79 32

3 0.73 32

4 0.39 31

4 0.39 31

4 0.39 31

4 0.39 31

4 0.56 41

4 0.6 44

5 0.25 19

5 0.25 19

5 0.25 19

5 0.25 19

5 0.58 45

6 0.59 46

6 0.07 5.74

115

6 0.08 6

7 6.68 257

7 0.92 45

7 0.9 44

7 0.9 44

7 0.9 44

7 0.9 44

7 2.03 98.31

7 1.16 57

7 1.19 58

7 1.19 58

7 1.19 58

7 1.17 57

7 5.02 233.83

7 5.91 287

8 3.43 136.66

8 1.04 45.27

8 0.9 44

8 0.9 44

8 1 49

8 1 49

8 0.98 48

9 1 49

9 1 49

9 1.52 67.51

9 1.83 89

9 2.2 106

10 236.78 131.94

10 171.18 194.45

10 106.13 172.89

11 277.23 166.07

11 57.16 164.47

11 264.78 159.65

11 264.69 159.63

12 158.22 151.52

12 258.3 149.9

12 1.21 59

12 1.16 48.35

13 1 49.03

13 0 0

13 0.82 38.27

14 1.28 62

14 1.26 62

116

14 68.33 339.04

14 117.57 161.9

14 4.67 164.39

15 3.8 181

15 3.7 180

15 3.71 180

15 2.24 109.18

15 4.74 216.08

15 4.7 216.71

15 4.53 216

15 4.53 216

15 4.54 216

15 4.48 213

15 2.89 108.81

16 0.74 31

16 0.74 31

16 0.76 31.76

16 0.79 33

17 0.49 24

17 47.07 120.76

17 0 0

18 0.2 14

18 5.55 244

19 5.45 239

19 5.45 239

19 0.01 1

19 0.41 24

19 0.52 29

20 0.67 36

20 0.66 35.54

20 0.1 8.24

20 0.23 18

21 0.24 19

21 0.24 19

21 0.25 20

21 0.25 20

21 0.25 20.04

21 0.25 20

21 0.23 18

21 14.35 554.09

21 2.96 191.97

21 2.51 189.61

21 2.43 190

117

22 9.09 440.97

22 8.36 405.79

22 8.37 406

22 9.06 439.73

22 8.99 436.88

22 9.87 479.94

23 11.03 534.15

23 9.59 463

23 2.39 117

24 2.35 115

24 2.35 115

24 1.82 89

24 1.8 88

24 1.81 88

24 2.61 97

24 2.77 87.53

25 2.13 104

25 1.25 51

25 1.09 52

25 1.03 50

25 1.05 51

25 1.05 51

25 1.55 75.06

25 1.58 76

25 2.91 90.06

25 1.43 68

26 1.46 71

26 1.46 71

26 4.64 226

26 3.63 176.45

27 3.59 171

27 3.71 170

27 3.76 170.22

27 1.35 52

27 1.35 52

28 1.18 40

28 1.18 40

28 3.09 147.11

28 2.95 144

28 2.95 144

28 2.95 144

28 3.84 164.86

28 6.66 202.66

118

29 3.49 170

29 3.48 170

29 6.24 301.11

29 7.32 313.56

30 6.07 230

30 10.85 410

30 10.84 407

31 7.74 302.68

31 11.02 342.78

31 7.43 274

32 0 0

32 0 0

32 0 0

33 137.3 48.64

33 65.92 235.59

33 129.29 231.06

34 71.78 224.51

34 2.11 120

34 0.47 22

35 0.63 31

35 0.59 26

35 10.02 377

36 10.03 377

36 10.04 377

36 9.87 371

36 9.88 371

36 52.69 153.45

36 112.45 145.35

36 170.68 105.01

36 279.69 32.34

36 311.14 3.8

36 41.11 446.71

36 69.04 345.08

36 190.62 209.87

37 98.15 86.82

37 156.72 72.36

37 31.38 131.5

37 104.82 124.57

37 132.92 115.93

37 142.09 101.03

37 227.11 186.07

38 0.02 0.77

38 0.16 8

119

39 2.23 97.08

39 2.18 97.56

40 28.24 354

40 1.15 51

40 7.4 246

40 6.49 247.35

40 24.82 248

40 5.72 245

40 0.68 36

41 4.94 227.36

41 5.39 283.73

41 5.97 352

41 0.59 32.84

41 1.83 66.23

41 0.15 8

41 0.17 10

42 3.46 89.2

42 4.21 93

42 4.12 103.87

43 0.04 3

43 0.04 3

43 2.67 98.42

44 0.26 13

44 0.98 48

44 14.94 488.46

45 1.79 88

45 1.17 24

45 2.27 134

45 2.27 134

45 2.27 134

46 2.74 161

46 2.74 161

46 2.74 161

47 2.74 161

47 2.81 165

47 2.77 163

48 2.76 162

48 2.76 162

48 2.78 163

48 2.76 162

48 2.74 161

49 2.76 162

49 0.24 14

120

49 0.24 14

49 0.24 14

49 0.24 14

50 0.24 14

50 0.24 14

50 0.26 15

51 0.7 41

51 0.72 42

52 0.72 42.55

52 12.42 474

52 12.48 475

52 5.54 210.93

52 0.03 1.19

52 0.04 2

52 2.15 105

53 5.75 279

53 5.74 279

53 2.16 105

53 25 43.88

53 0.6 13

53 0.26 5.9

53 5.8 281

53 4.04 193.77

53 4.97 239

53 4.96 239

53 4.95 239

54 5.85 283

55 2.29 112

56 4.74 231

56 2.83 138

56 0.01 1

57 0.92 41

57 0.33 15

57 0.67 52

57 0 0

58 0.32 14

59 0.23 18

59 0.23 18

59 0.23 18

59 3.24 143

59 3.21 141

59 0.03 1

59 1.96 74.08

121

59 3.14 87.51

60 3.65 166.04

60 3.42 97

60 3.42 97

60 3.42 97

61 0 0

61 0 0

61 0 0

61 0 0

62 0.92 23.82

62 5.73 252

63 7.56 372

63 8.49 418.17

63 8.4 421.45

63 8.41 422

63 8.39 421.28

63 8.41 421.88

63 8.42 422.54

63 21.64 870.75

63 34.16 1116.76

64 6.83 441.59

64 2.85 176.88

64 1.78 85.49

64 2.05 60.47

64 0 0

64 13.75 148.97

64 63.39 230.46

64 20.08 584.78

64 106.02 561.53

65 1.91 31.85

65 2.23 97

65 1.74 77

65 10.52 458

65 0.06 3

66 0.04 1.91

66 0.18 9

66 0.18 9

67 77.79 427.96

67 115.41 410

67 0 0

67 0.91 14.63

68 229.29 177.32

68 186.95 171.64

122

68 29.34 65.55

69 0.68 33

69 0.68 33

69 0.68 33

69 0.04 2

70 0.04 2

70 0.04 2

70 0.04 2

70 0.04 2

70 0.04 2

70 0.04 2

70 0.28 11.47

70 0.64 30

71 11.87 103.11

71 124.23 20.71

71 0.37 10

71 272.57 45.34

72 0.47 1.49

72 0 0

73 0.34 26

74 0.13 10

74 4.05 154

74 4.04 154

75 0 0

75 116.35 286.91

75 171.08 395.09

75 0.14 7

75 0.14 7

75 0.14 7

75 0.14 7

75 0.14 7

75 0.14 7

75 1.16 57

75 2.85 107

76 2.78 136

76 3.05 149

76 3.05 149

123

Table A-8: Macroscopic Link Properties for High EV scenario


1 0.66 26.25

1 1.47 56

1 2.91 113.57

1 15.06 519

1 3.21 123

1 3.21 123

1 3.18 122

2 3.21 123

2 3.21 123

2 3.21 123

2 3.25 124

2 3.25 124

2 3.25 124

2 0.93 41

2 0.94 41

2 0.18 14

2 0.18 14

2 0.18 14

2 2.18 99

3 0.39 17

3 0.39 17

3 0.63 28

3 0.79 33

3 0.76 33

4 0.35 28

4 0.35 28

4 0.35 28

4 0.35 28

4 0.52 37

4 0.66 45

5 0.17 13

5 0.17 13

5 0.17 13

5 0.17 13

5 0.56 44

6 0.56 44

6 0.09 6.74

6 0.09 7

7 6.68 257

124

7 1 49

7 1 49

7 1 49

7 1 49

7 1 49

7 2.26 109.49

7 1.32 64

7 1.32 64

7 1.32 64

7 1.32 64

7 1.34 65

7 5.17 239.94

7 5.69 277

8 3.15 125.59

8 1.3 56

8 1.24 61

8 1.24 61

8 1.24 61

8 1.24 61

8 1.24 61

9 1.08 53

9 1.08 53

9 1.56 69.28

9 2.05 100

9 2.64 126.55

10 168.43 130.5

10 76.65 227.47

10 151.61 250.63

11 260.84 216.74

11 245.73 215.59

11 258.71 178.71

11 251.75 177.08

12 171.68 151.59

12 246.94 148.62

12 1.64 80

12 1.28 52.95

13 1.03 50.12

13 0 0

13 1.28 60

14 1.45 70

14 1.44 71

14 66.54 340.6

14 214.9 154.33

125

14 4.99 175.04

15 4.01 188.4

15 4.03 196

15 4.08 198

15 2.69 130.5

15 5.07 230.22

15 5.08 232.62

15 4.81 228

15 4.75 225

15 4.78 226

15 4.73 224

15 3.84 144.95

16 0.88 38

16 0.88 38

16 0.91 38.76

16 1.14 47

17 0.78 38

17 18.33 142.75

17 1.05 51

18 0.2 14

18 5.52 243

19 5.41 237

19 5.41 237

19 0.01 1

19 0.5 28

19 0.54 30

20 0.61 33.16

20 0.64 34.35

20 0.1 8.24

20 0.23 18

21 0.26 20

21 0.26 20

21 0.27 21

21 0.27 21

21 0.26 20.04

21 0.26 20

21 0.24 18

21 14.35 554.09

21 2.96 191.97

21 2.51 189.61

21 2.43 190

22 8.66 420.3

22 8.29 402.07

126

22 8.27 400.73

22 8.88 433

22 8.81 429.62

22 9.64 470.44

23 10.75 522

23 9.75 472.15

23 2.19 107

24 2.19 107

24 2.18 106

24 1.97 96

24 1.97 96

24 1.97 96

24 2.56 95.66

24 2.97 91.98

25 2.48 122

25 1.88 78

25 1.63 79

25 1.64 80

25 1.64 80

25 1.66 81

25 2.5 122

25 2.53 122

25 4.13 131.63

25 2.21 104

26 2.22 108

26 2.22 108

26 4.47 218

26 4.09 198.33

27 4.08 197.56

27 4.1 197.92

27 4.09 197

27 2.18 83.1

27 2.31 88

28 1.28 43

28 1.28 43

28 3.14 149.22

28 3.03 148

28 3.03 148.35

28 3.05 149

28 3.86 158.64

28 7.01 213.41

29 3.69 180

29 3.76 183

127

29 6.52 314.14

29 6.94 297.25

30 5.5 210

30 10.67 402.39

30 10.77 404

31 7.4 289.58

31 12.2 376.96

31 6.84 255

32 1.03 50

32 0.38 17

32 1.07 52

33 115.06 83.14

33 94.99 279.5

33 98.73 274.65

34 53.49 262.59

34 2.11 120

34 0.39 20

35 0.8 39

35 0.6 27

35 9.19 345

36 9.1 341

36 9.12 341

36 9.12 341

36 9.18 342

36 51.95 164.69

36 42.23 156.62

36 146.19 116.06

36 128.68 42.45

36 148.34 9.55

36 60.8 489.25

36 98.34 385.86

36 180.09 254.89

37 104.35 111.08

37 182.17 92.81

37 52.18 136.93

37 103.59 126.32

37 104.52 112.83

37 136.64 98.11

37 170.73 203.77

38 0 0.22

38 0.04 2

39 2.27 98.68

39 2.19 98.11

128

40 26.91 354

40 1.3 57

40 7.86 264

40 6.93 265.35

40 25.56 266

40 5.76 245

40 0.64 34

41 4.94 227.36

41 5.39 283.73

41 5.97 352

41 0.59 32.84

41 1.83 66.23

41 0.15 8

41 0.17 10

42 3.79 97.47

42 4.54 97.3

42 4.16 104

43 0.03 2

43 0.03 2

43 2.61 101.17

44 0.22 11

44 0.94 46

44 14.51 474.34

45 1.87 92

45 2.06 33.96

45 2.05 121.05

45 2.07 122

45 2.07 122

46 2.82 166

46 2.88 169

46 2.88 169

47 2.88 169

47 2.93 172

47 2.91 171

48 2.91 170.69

48 2.9 170

48 2.92 171

48 2.88 169

48 2.86 168

49 2.83 166

49 0.32 19

49 0.32 19

49 0.34 20

129

49 0.34 20

50 0.34 20

50 0.32 19

50 0.34 20

51 0.77 45

51 0.82 48

52 0.82 48

52 12.38 472

52 12.42 472.96

52 5.51 209.6

52 0.02 0.91

52 0.02 1

52 2.24 109.02

53 5.68 276

53 5.66 275

53 2.28 111

53 14.26 43.89

53 0.98 21.03

53 0.42 9.47

53 5.68 276

53 4.14 198.43

53 5.1 245

53 5.08 245

53 5.08 245

54 5.64 274

55 2.36 115

56 4.55 221

56 2.74 134

56 0.01 1

57 0.92 41

57 0.33 15

57 0.67 52

57 0 0

58 0.32 14

59 0.23 18

59 0.23 18

59 0.23 18

59 3.3 146

59 3.27 144

59 0.05 2

59 2 75.8

59 3.15 87.84

60 3.28 148

130

60 3.42 97

60 3.42 97

60 3.42 97

61 0 0

61 0 0

61 0 0

61 0 0

62 1.24 32.29

62 5.75 252

63 8.15 402

63 8.84 434.05

63 9 448.73

63 9 449

63 8.96 447.14

63 8.96 447

63 8.93 445.98

63 20.58 894.87

63 30.37 1150.78

64 6.56 426.54

64 2.69 161.8

64 1.66 77.25

64 1.88 52.45

64 0 0

64 13.92 162.34

64 64.26 223.8

64 19.12 585.9

64 110.42 547.62

65 2.68 36.1

65 2.35 102

65 1.74 77

65 10.33 450

65 0.06 3

66 0.06 2.72

66 0.18 9

66 0.18 9

67 101.2 473.09

67 102.2 451.83

67 0 0

67 1.15 18.8

68 225.59 200.69

68 235.9 194.2

68 43.71 68.1

69 1.19 58

131

69 1.19 58

69 1.19 58

69 0.06 3

70 0.1 5

70 0.1 5

70 0.1 5

70 0.1 5

70 0.12 6

70 0.12 6

70 0.82 28.92

70 1.36 60.95

71 54.62 121.91

71 216.99 24.47

71 0.47 10

71 260.71 70.97

72 16.19 6.17

72 0 0

73 0.38 28

74 0.13 10

74 4.8 183

74 4.79 183

75 0 0

75 120.82 279.99

75 149.69 378.69

75 0.04 2

75 0.04 2

75 0.04 2

75 0.04 2

75 0.04 2

75 0.04 2

75 0.98 48

75 2.61 98.05

76 2.54 124

76 3.21 157

76 3.21 157

New USING MACROSCOPIC FUNDAMENTAL DIAGRAM TO … · 2020. 9. 28. · USING MACROSCOPIC FUNDAMENTAL...

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