Development of Congestion Maps for Selected Corridors of Dhaka City Using Instrumented Vehicle

DEVELOPMENT OF CONGESTION MAPS FOR SELECTED CORRIDORS OF DHAKA CITY USING INSTRUMENTED VEHICLE.

MST. TAHNIN TARIQ

DEPARTMENT OF CIVIL ENGINEERING BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY (BUET) Dhaka, Bangladesh

November, 2015

DEVELOPMENT OF CONGESTION MAPS FOR SELECTED CORRIDORS OF DHAKA CITY USING INSTRUMENTED VEHICLE.

Bangladesh University of Engineering and Technology

A THESIS SUBMITTED TO THE DEPARTMENT OF CIVIL ENGINEERING IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE DEGREE OF MASTERS OF SCIENCE IN CIVIL ENGINEERING (TRANSPORTAION)

A THESIS SUBMITTED BY MST. TAHNIN TARIQ DEPARTMENT OF CIVIL ENGINEERING BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY (BUET) Dhaka, Bangladesh November, 2015

This thesis titled "Developmenl of congestion maps lbr selecled corridors ol' Dh:rkn cify using instrumented vehicle.". submitted by Mst. Tahnin Tariq. Iloll No.

0412042405F, Session: Aplil 2012, has been accepted as satisthctory in prrt ia I fulfilhnent ol the requirernent for the degree of Master of Science in Civil

Engineering (Transportation) on I 0rl' Novenrber, 201 5.

BOARD OF EXAMINERS (ass_- Dr. 'fanrveer Hasan. Chairman Professor (SLrperv isor.; Depaftnlent of Civil Engineering, BUET

Dr. K.A.M. Abdul Muqtadir Menrber Professor and Head (Ex-ot'ficio) Department ol Civil Engineering. BUET

Dr. Md. Mizanur Rahman Member Prolessor Department of Civil Engineering, BUET

'6:u3 r.^"-- IL-''-h *-o t't- Dr. Farzana Rahman Memher Associate Prot'essor ( External) Depa ment olCivil Engineering. University of Asia Pacitic (UAP) CANDIDATE'S DECLARATION

Declared that, the studies in this thesis is the result of research work by author, except for the contents where specific reference has been made to the work ofothers.

The thesis or any part of the thesis has not been submitted to any other university or educational institute for the award for any degree, except for publication.

D),.n)'r., (Mst. Tahnin Tariq)

DEDICATION

This Thesis is dedicated to my parents.

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ACKNOWLEDGEMENTS

At first, the author acknowledges the blessings of almighty, kindness, merciful, the universal king, Allah for enabling her to complete this thesis successfully.

The author indebted to her wonderful parents for their invaluable guidance, love and affectation throughout her entire life. She would like to thank them for their continuous blessing and encouragement in every step of getting education.

The author wishes her profound gratitude and sincere appreciation and thanks to her honourable and respected supervisor Dr.Tanweer Hasan, Professor, Department of Civil Engineering, BUET, Dhaka for his keen interest in this thesis and thoughtful ideas, continuous guidance and demonstrative encouragement at every stage of this study. His careful reading of the draft of thesis, valuable comments and fruitful suggestions greatly contributed to the development of the thesis. His enthusiastic supervision is the most respectful achievement of the life of author and also remains forever.

The author wishes to express her gratitude and thanks to her respected defence committee members Dr. Abdul Muqtadir, Professor and Head, Department of Civil Engineering, BUET; Dr. Md. Mizanur Rahman, Professor, Department of Civil Engineering, BUET; Dr. Farzana Rahman, Associate Professor, Department of Civil Engineering, University of Asia Pacific (UAP); for their valuable advice and directions in reviewing this thesis.

The author pays her deepest homage to her family members and friends who helped her with necessary advice and moral support during this thesis work.

The author thankfully acknowledges the financial support provided by the Committee for Advanced Studies and Researches (CASR), BUET.

Abstract

The knowledge of travel times on road networks has vital importance to measure network performance and quantify congestion for road users as well as for network operators. Congestion of an urban road network varies in different times of a weekday and weekends. Time varying congestion heat maps are easily understood by all transport users. And time varying contour maps are effective way to estimate the network performance at various times by transportation planners. In this study variation of travel time on a selected road network of Dhaka city is estimated by developing congestion heat maps and contour maps by using MATLAB- programming software. And also shortest possible route for a certain Origin - Destination pair has been tested by using shortest path algorithm. Average Intersection delay is estimated for both week days and weekend days in this study.

In this study, travel time data collection technique has been selected, the data collection by GPS-enabled devices such as, smart phone. GPS enabled vehicles provide an alternative to other travel time data collection methods with decent accuracy and can be used for providing real time traffic condition of the network. As Dhaka city has the high penetration of smart phone market so it is possible to extract a huge data set of travel time of a road network by this data collection method.

The data processing and data analysis part is mainly done by the programming software- MATLAB which processes huge set of data in a short time integrating with visualization.

Generated heat maps and contour maps shows a significant change in congestion from 1st slot (8.00.00AM- 11.59.59AM) to 2nd slot(12.00.00PM-3.59.59PM) , 3rd (4.00.00 PM-7.59.59PM) and 4th slot (8.00.00PM-10.59.59PM) on weekday. And for weekend maps shows congestion criteria is mostly haphazard pattern, though most of the congestion occurs on 2nd slot (12.00.00PM-3.59.59PM) and 4th slot (8.00.00PM-10.59.59PM) on weekend.

The shortest path estimation for the O-D pair 48(23.742651, 90.395722, Ruposhi Bangla Mor) to 37(23.727629, 90.410457, Shohid Noor Hossain Square) is done by

Dijkstra's Algorithm, which shows a shortest path for this pair varies in weekday but is almost same for weekend on this pair.

As this study shows the route choice of the road users can vary in different time if the real time congestion maps are available. Thus this model developed in this thesis can be used in further study to develop a real time congestion analysis for the full network of any congested city in Bangladesh and construct route choice tools for the road users.

Table of Contents

Page Candidate’s declaration ...... ii Dedication ...... iii Acknowledgements ...... iv Abstract……………………………………………….…………………………………….v Table of Contents ...... vii List of Figures ...... ix List of Table ...... xi Chapter 1 ...... 1 Introduction ...... 1 1.1 Background and problem statement: ...... 1 1.2 Objectives of the study ...... 2 1.3 Study Area……………………………………………….……………….………3

1.4 Scope and limitations………………………………..…….……….…..…………. 4 1.5 Organization of the Thesis ...... 5 Chapter 2…………………………………………………………………………….6 Literature Review………………………………………………………….………..6 2.1.Travel time estimation in Transportation planning………...……………….…….6 2.2Method of Travel time...... 8 a. Test Vehicle method ...... 8 b. License Plate matching method ...... 10 c. Probe Vehicle Technique ...... 10 d. Emerging and Non-Traditional method ...... 13 2.3 Visualization Travel time Information ...... 15 2.3.1 Contour Map in transportation planning……………………………….15

2.3.2 Heat map as congestion map……………………………………….…..16 2.4 Evaluation of Route-choice …………………………………………………….17 2.5 Other Shortest Path Algorithms……………………………………………...... 18 2.6 Intersection Delay Study…………………………………………..……………19 Chapter 3………………………………………...... 21

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Methodology………………………………………………………………………..21 3.1 Introduction…….…………………………………………………..…………...21 3.2 Study Corridors ...... 21 3.3 Data Collection & preprocessing ...... 23 3.3.1 Data Collection………………………………………………………...23 3.3.2 Data Processing………………………………………………………..24 3.4 Preparing Travel time look-up table……………………………………………26 3.5 Development of Contour map…………………………………………………...29 3.6 Development of Heat map...... 31 3.7 Evaluation & Demonstration ...... 31 3.8 Calculation of Intersection Delay for week day and weekend……..………..….32 Chapter 4 ...... 34 Data Analysis and Results ...... 34 4.1 Data Processing…………………………………………………………..…...... 34 4.2 Data Analysis ...... 35 4.2.1 Development of Contour Map ...... 35 4.2.2 Development of Heat Map ...... 43 4.3 Evaluation of the Congestion maps ...... 52 4.4 Evaluation of Intersection Delay……………………………………………...... 61 Chapter 5 ...... 62 Conclusion and Recommendation ...... 62 References ...... 64 Appendices ...... 67 Appendix A:Data formation-conversion of unix time ...... 68 Appendix B: Details of nodes and links……………………………………...……..73 Appendix C: Estimated Travel time and Speed Data of various links ...... 81 Appendix D:Intersection Delay Data ...... 126 Appendix E: Detail code-MATLAB programming software………….…………..132

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List of Figures Figure 1.1: Map of study area ...... 4 Figure 2.1: An Example of travel time contour map………………………………..16 Figure 2.2: An example output of traffic estimateson the mobile millenium visualizer ...... 17 Figure 3.1: Work-flow diagram for traffic congestion study ...... 21 Figure 3.2: Selected Study area ...... 22 Figure 3.3: Links and nodes on selected road network ...... 22 Figure 3.4: Collected Data format ...... 23 Figure 3.5: Lattitude and Longitude Data plotted in Google Map………………….25 Figure 3.6: Plot of only node data ...... 27 Figure 4.1. Contour map for week day on 8.00.00 AM-11.59.59 AM………………35 Figure 4.2. Contour map for weekend day on 8.00.00 AM-11.59.59 AM…………36 Figure 4.3. Contour map for week day on 12.00.00 PM-3.59.59 PM………………37

Figure 4.4. Contour map for weekend day on 12.00.00 PM-3.59.59 PM………….…38

Figure 4.5. Contour map for week day on 4.00.00 PM-7.59.59 PM……………..……39 Figure 4.6. Contour map for weekend day on 4.00.00 PM-7.59.59 PM……………40 Figure 4.7. Contour map for week day on 8.00.00 PM-11.59.59 PM………………41

Figure 4.8. Contour map for weekend day on 8.00.00 PM-11.59.59 PM………..…42

Figure 4.9. Heat map for week day on 8.00.00 AM-11.59.59 AM…………….……43 Figure 4.10. Heat map for weekend day on 8.00.00 AM-11.59.59 AM…………...44 Figure 4.11. Heat map for week day on 12.00.00 PM-3.59.59 PM……………...…45

Figure 4.12. Heat map for weekend day on 12.00.00 PM-3.59.59 PM………………46

Figure 4.13. Heat map for week day on 4.00.00 PM-7.59.59 PM………………...……47 Figure 4.14. Heat map for weekend day on 4.00.00 PM-7.59.59 PM…………..….48

Figure 4.15. Heat map for week day on 8.00.00 PM-11.59.59 PM…………………49

Figure 4.16. Heat map for weekend day on 8.00.00 PM-11.59.59 PM……….....….50 Figure 4.17: Shortest path and time for 48-37 O-D pair on weekday 8.00.00 AM- 11.59.59 AM ……………………………………………………………………….53

Figure 4.18: Shortest path and time for 48-37 O-D pair on weekday 12.00.00 PM- 3.59.59 PM ……………………………………………………………………..…..54 Figure 4.19: Shortest path and time for 48-37 O-D pair on weekday 4.00.00 PM- 7.59.59 PM ……………………………………………………………………...….56 Figure 4.20: Shortest path and time for 48-37 O-D pair on weekday 8.00.00 PM- 11.59.59 PM ………………………………………………………………….…….57 Figure 4.21: Shortest path and time for 48-37 O-D pair on weekend day 8.00.00 AM-11.59.59 AM …………………………………………………………….…….58 Figure 4.22: Shortest path and time for 48-37 O-D pair on weekend day 12.00.00 PM-3.59.59 PM …………………………………………………………………….59 Figure 4.23: Shortest path and time for 48-37 O-D pair on weekend day 4.00.00 PM- 7.59.59 PM ……………………………………………………………………...….60 Figure 4.24: Shortest path and time for 48-37 O-D pair on weekend day 8.00.00 PM- 11.59.59 PM …………………………………………………………….………….61

List of Table Table 3.1. Sorted Unix time Data in ascending order ...... 28 Table 3.2. Sorted Unix time Data in ascending order……………………..………….32 Table 4.1: Shortest Path and time for 48-37 O-D pair on Week Day ...... 52 Table 4.2: Shortest Path and time for 48-37 O-D pair on weekend day ...... 57 Table 4.3: Average Intersection Delay in weekend and weekdays…………………….61 Appendix A Section A.1.1.: Sample Data set after converting Unix time in Year- Month-Day-Hour-Minute-Second format ...... 68 Appendix A Section A.1.2.: Sample Data set after sorting only for node Data for weekend days and weekdays……………………………………………………….69 a) Sample Data set after sorting only for node Data for weekend days ...... 69 b) Sample Data set after sorting only for node Data for weekdays…………….70 Appendix A Section A.1.3.:Sample Data set after sorting only for IN-TIME node Data for weekend days and weekdays……………………………………………..71 a) Sample Data set after sorting only for IN-TIME node Data for weekend days…………………………………………………………………………71 b) Sample Data set after sorting only for IN-TIME node Data for weekdays…71 Appendix B Section B.1.1.:Node‟s Number, Latitude and Longitude ...... 73 Appendix B Section B.1.2.:Length of the links………………………………….…75 Appendix C Section C.1.1.:Travel time of the links for weekdays and weekend days……………………………………………………..…………………..81 a) Travel time of the links for weekdays ...... 81 b) Travel time of the links for weekend days………………………………….90 Appendix C Section C.1.2.:Travel time of the links in different time for weekdays and weekend days…………………………………………………………………110

a) Travel time of the links in different time for weekend days……..………..110 b) Travel time of the links in different time for week days……………………113

Appendix C Section C.1.3.: TravellingSpeed of different links for different times of weekdays and weekends………………………………………………………….117 a) TravellingSpeed of different links for different times of weekend days…117 b) TravellingSpeed of different links for different times of week days…..….121

Appendix D Section D.1.1.:Intersection delay data for weekdays and weekends…………………………………………………………………………..126

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Chapter 1

Introduction

1.1 Background and problem statement:

Travel time is a vital operational measure of traffic condition for transportation planning and traffic management. It can present variety of traffic congestion at different time in the network. By acquiring accurate travel time information transportation planners can make more informed decisions for traffic management and prioritize the network improvement plan through identifying congested road section. From the road user‟s point of view, time depended travel time information can help in selecting alternative roads to travel certain origin-destination pair at specific time of day in the shortest possible time.

Travel time can be estimated in different ways. Floating car observer, loop detectors, vehicle identification devices, number plate identification etc. (Lin and Zito, 2005) are some of the well-established methods of the travel time estimation, which are time consuming and costly as well. (Jensen and Larsen, 2007). However, now a days, hand held GPS machine or GPS enable mobile phones are being used for estimating real-time travel time in various researches. This new approach is more convenient to operate as well as cost saving. (Chen et al., 2009). GPS enabled mobile phones minimize the installation and operating cost of using probe instruments and sensors, and it can become a potential source of traffic data. (Tao et al.,2011)

In recent year number of studies in many developing countries like India, Thailand, china etc. using probe vehicle data for estimating real time travel time using movable sensors. (Puangprakhon and Narupit, 2013).Hence, by extracting data from vehicles instrumented with GPS, substantial number of travel time observations in term of space and time can be obtained and used for congestion forecasting at different times with a low installation and operating cost with a decent level of location accuracy.

Bangladesh is a country with very high cell phone market penetration (Ching et al., 2012). In Dhaka city there are many smartphone users from which GPS data can be

extracted easily from internal GPS facility, internet connectivity and the nearby mobile towers. Thus there is a huge opportunity to use this developed technology for collecting travel time data with high accuracy.

On the other hand, visualization plays important role in understanding large and complex data sets for drivers and other road users. In this research, a new method for representing travel time in road networks is proposed my map matching GPS observations. The basic idea of this study is to display travel variation visually by means of travel time contour map and travel time heat map using MATLAB.

In Travel time contour map contours displays the assessment of travel time in various direction for a center point. This map can be used for transportation planning and identify the network segment that need to improve (Prassaset al., 2004).

In travel time heat map color variation indicates congestion level in various times of a day and days of week for each link. Level of congestions in terms of average speed, is expressed with pre-defined color scale (Wu and McLaughlin, 2012). Engineers and policy makers can use this information for travel demand management, and serve as vital information for both road users and traffic operators of Dhaka city.

Travel time variation pattern in Dhaka city is not acknowledged by traffic planners and for which it is not possible to forecast freeway and congested way for any situation. Though now a days GPS system is available for most of the road users, there is no method is available for using GPS to avail or visualize traffic information. So, it is important to estimate to what extent travel time varies based on time of day and day of week and establish new method to visualize using available equipment and in lowest operating cost.

1.2 Objectives of this study:

The main objective of the study is to investigate the possibility of using probe vehicle data to estimate time-varying travel time in context of a developing city and employ the outcome in facilitating policy level decision making in the transportation sector. The specific objectives of this study are given below:

1. Employ probe vehicle data to estimate average travel time and it‟s time dependent variation for different links of Dhaka city road network.

2. Generate time dependent travel time congestion map for selected corridors of Dhaka city in form of travel time heat-maps and travel time contour-maps.

3. Apply travel time heat-maps and contour-maps to identify bottle necks and suggest future projects to reduce traffic congestion within the study area.

1.3 Study area:

Some selected corridors of Dhaka city, the capital of Bangladesh, have been chosen as the study area. Dhaka city is the center of political, cultural, economic life of Bangladesh consisting population of over 15 million with a density 8229 per sq. km. (BBS, 2011). Dhaka‟s road network is nearly 3000 km (STP, 2004), of which 200 km primary roads, 110 km secondary roads, 50 km feeder roads and rest 2640 km narrow roads, with few alternatives and connector road. Transport mode of Dhaka can be mainly classified into two groups, motorized vehicle and non -motorized vehicle. Motorized vehicles are mainly bus, mini-bus, truck, car, auto-rickshaw, auto- tempo, motorcycle etc. and non-motorized vehicles are rickshaw, rickshaw van, bicycle, push cart etc. This research covers the corridors Kazi Nazrul Islam Avenue, S Captain Munsur Ali Avenue, Shaheed Tajuddin Ahmed Avenue, DIT Road and Mirpur Road which consist Dhanmondi Thana, Ramna Thana, Tejgao Thana, Motijheel Thana, New Market Thana, Rampura Thana, Panthapath, Shahbag, PaltanThana and a part of Badda Thana of Dhaka metropolitan city. Selected study area on the Dhaka city map is shown in Figure below:

Figure 1.1: Map of study area.

1.4 Scope and limitations:

As mentioned earlier, this study covers only a selected part of Dhaka city. Also, a part of the data collection was conducted during a time period where political strikes were going on. This has substantially reduced the travel time. It is recommended to use caution in interpreting the results in the context of numerical values of travel time only. Rather, the highlight of this study is the research framework which addresses how probe vehicle data can be used for policy implications.

1.5 Organization of the thesis:

The study consists of six chapters. Chapter 1 introduces this research, puts forward to be solved, sets the objectives to be achieved, and states scope and limitations. Chapter 2 presents a review of the literature related to data collection, their use, travel time estimation techniques and applications as well as the use of probe vehicle data in the field of transportation. Chapter 3 outlines the methodology describing the procedure of using GPS data and visualization as well as interpreting the results in context of future transportation planning. Chapter 4 presents data analysis and develops the travel time congestion maps. Chapter 5 describes the evaluation of time varying analysis and bottleneck situation of the network of Dhaka city and identifies potential traffic improvement possibilities. Chapter 6 summarizes the results and recommends issues to be considered for future researches. It also discusses about the future possible extensions of the study.

Chapter 2 Literature review

In this thesis, travel time estimation at various times of a day and various days of a week is visualized by two types of congestion map, “travel time contour map” and “travel time heat map”. The later part of the research creates a hypothetical situation where the variation of route choice is evaluated for a selected Origin-Destination pares. Based on the results the benefits of real-time traffic information on network are investigated. Here, travel time data are collected by smart phones placed in vehicles in traffic stream which acted as probe vehicles.

The review of the literature will briefly explore the use of travel time information in transportation engineering, various methods used for travel time estimation, practice of representation of travel time data as heat maps and contour maps and finally, available classical shortest path algorithms.

2.1. Travel time estimation in Transportation Planning:

Travel time is the required time to travel from one point to another, as it can be composed into free flow travel time (the minimal travel time without congestion) and delay. Actual travel time is of link can be summarize as the sum of free flow time, systematic delay and unexplained delay on that link. (Fosgerau et al., 2008). Travel time is the most efficient measure traffic congestion. It is a quantitative parameter for representing traffic condition. Travel time information can be used in various fields of transportation engineering and logistics. (Prassas et al., 2004). Travel time data is useful for a wide range of transportation analyses including congestion management, transportation planning, and traveler information.

Travel time study of a study area has many uses including:

- Evaluating and monitoring traffic congestion using travel time-based performance measures. - Identification of problem locations on a study area according to higher travel time or delay. - Evaluating the level of service based on average speed and travel time.

- In different traffic assessment model travel time can be used as key determinant for route choice of a selected road network. - Economic evaluation of improvement different routes using travel time data.

In travelers point of view, by knowing travel time information travelers can save time and improve reliability by selecting best suitable route before and during the trip. To Increase the involvement of taking transportation decisions by non-technical person (politicians, advocacy groups, and the general public) requires an output which is simple and easy to understand but have analytical measures. . Travel-time information is an essential input data for specialists of traffic system operation to reduce the delivery costs, increase the reliability of delivery, and improve the service quality. To fulfill this requirement travel time estimation and visualization is the appropriate way.

The accurate travel time data is a crucial component for Intelligent Transport System (ITS) applications, such as traffic management system (ATM), in-vehicle route guidance systems (RGS). In the research of Vanajakshi, real-time travel-time data is estimated using loop detector and implemented in ITS applications for short-term travel time prediction. (Vanajakshi, 2004). Travel time depends on various traffic factors. To improve the prediction accurately it is important to understand these factors, which is difficult task and needs huge amount of traffic data. ITS enriches motorists system with enroute information that is predicted travel times data (up to date recommended) to the Portable Changeable Message Signs (PCMS) for display. This system is activated by constantly obtaining data from microwave traffic sensors and also the section of roadway is monitored. This data is sent to a base station where different calculation for travel time prediction is conducted by the software. Then data is housed in the project field office. The base station can be connected to the Internet to provide information to motorists, before leaving their home or office. By observing displayed travel times the motorists can make an informed decision on which routes to take which reduces the stress and anxiety. Motorists can reduce the demand on an alternate route when travel times of a road are high due to roadwork or an incident. A database can be maintained to advise motorists if the travel times are

displayed on the Internet. Also, graphs showing the average travel times can be provided over the Internet.

2.2 Methods of travel time estimation:

There are several methods for travel time data collection. . These methods are designed to collect travel times and average speeds on specific roadway segments or links.

These travel time data collection methods are:

a. Test vehicle method b. License plate matching method c. Probe vehicle method d. Emerging and Non-Traditional method i. Extrapolation Method ii. Vehicle Signature Matching iii. Platoon Matching iv. Aerial Surveys

a. Test vehicle method: The test vehicle technique has been used for travel time data collection since the late 1920s. This technique uses a data collection vehicle. Within this vehicle cumulative travel time is recorded at checkpoints defined earlier by an observer which converts to travel time, speed, and delay for each segment along the survey route. Different methods are performed for this type of data collection, depending upon the instrumentation used in the vehicle and the driving instructions given to the driver. These vehicles, known as “active” test vehicles are instrumented and then sent into the field for travel time data collection. Vehicles that are already in the traffic stream for actions other than data collection are referred to as “passive” ITS probe vehicles.

Three levels of instrumentation used to measure travel time with a test vehicle are described below:

 Manual - According to the history of travel time data collection technique, the manual method has been used mostly. Elapsed time is recorded manually at predefined check points. This method requires a driver and a passenger to be in the test vehicle. The driver operates the test vehicle while the passenger records time information at predefined checkpoints.  Distance Measuring Instrument (DMI) - Technology has automated the manual method with the use of an electronic DMI. The DMI is connected to a portable computer in the test vehicle and receives pulses at given intervals from the transmission of the vehicle. Distance and speed measured from these pulses received from the electronic DMI determines travel time along a corridor.  Global Positioning System (GPS) - GPS has become the most recent technology to be used for travel time data collection. A GPS receiver is connected to a portable computer and collects the latitude and longitude information. Using these signals position and speed of the test vehicle is determined and the vehicle can be tracked. The following elements are included for the techniques of this system: overview, advantages and disadvantages, cost and equipment requirements, data collection instructions, data reduction and quality control, and previous experiences. Since the driver of the test vehicle is a member of the data collection team, to match desired driving behavior his driving styles and behavior can be controlled. The following are three common test vehicle driving styles:  Average car - test vehicle travels according to the driver‟s judgment of the average speed of the traffic stream.  Floating car - The floating car driving style is the most commonly driving style. Driver “floats” with the traffic by attempting to safely pass as many vehicles as pass the test vehicle.  Maximum car - test vehicle is driven at the posted speed limit unless impeded by actual traffic conditions or safety considerations.

In practice, drivers usually adopts a hybrid of the floating car and average car because of the inherent difficulties of keeping track of passed and passing vehicles in high traffic volume conditions.

Test vehicle techniques have the following advantages:

 Provides for the determination of driving styles (e.g., “floating car”), for which consistent data can be collected.  Advanced test vehicle techniques (e.g., DMI or GPS use) provide detailed data covering the entire study corridor.  Relatively low initial cost.  Test vehicle techniques have the following disadvantages:  Sources of possible error from either human or electric sources that require adequate quality control.  Advanced and detailed data collection techniques (e.g., every second) can provide data storage difficulties.  The travel time estimation for the corridor is based on only one vehicle that is in the traffic stream. b. License plate matching method Generally license plate matching techniques are comprised of collection of vehicle license plate numbers and arrival times at various checkpoints, comparing these numbers between consecutive checkpoints, and measuring travel times obtained from the difference in arrival times. Following are the four basic methods of collecting and processing license plates:

 Manual: Keeping information of license plates through pen and paper or audio tape recorders and entering these data of plates and arrival times into a computer manually.  Portable Computer: collecting license plates automatically arrival time stamp information in the field using portable computers  Video with Manual Transcription: collecting license plate information using video record and manually entering the data.

 Video with Character Recognition: collecting license plate information using video record and automatically entering the data by using computerized vehicle plate recognition.

Advantages of using license plate matching method:

 Travel time data can be obtained times from a large sample of motorists, from that variability of travel time among the vehicles can be understand easily.  Provides a continuum of travel times during the data collection period and ability to analyze short time periods  Data collection equipment relatively portable between observation sites.

Disadvantages of using license plate matching method:  Travel time data limited to locations where observers or video cameras can be positioned;  Limited geographic coverage on a single day;  Manual and portable computer-based methods are less practical for high- speed freeways or long sections of roadway with a low percentage of through-traffic;  Accuracy of license plate reading is an issue for manual and portable computer-based methods  Skilled data collection personnel required for collecting license plates and/or operating electronic equipment.

c. Probe vehicle technique:

Probe vehicle technique is designed for collecting travel time data in real time. Probe vehicles are equipped with GPS receiver and communicate to receive signals from earth orbiting satellites. In the situation of poor GPS connection the equipment receive data from mobile towers. GPS data contains travel location and time. Probe vehicle system for travel time data collection has some advantages and disadvantages.

Advantages of probe vehicle system:

 Low cost system: The data collection procedure is easy and low cost after necessary equipment is installed.  Automated and continuous data collection: Travel time data can be collected continuously for 24 hours a day by this method without interruption. As the data collected and saved by the equipment itself, data can be automatically transmitted.

Disadvantages of probe vehicle system:

 High implementation cost: The system have high initial cost for installation of costly equipment.  Requirement of skilled operator or software design: To operate the instruments trained and skilled person is required.  Need extra effort to process large number of data in a precise form.

The main reason of using vehicle as probe is to use the vehicle as representative of actual traffic flow condition. Using GPS instrumented vehicles are used as probe (Sanwal and Walrand, 1995), flow variable can easily be estimated and error of the traffic data shows a regular static manner.

Advantages of using GPS enabled mobile phone is when the accuracy suffers due to loss of the GPS signals, mobile towers assist to get correct location and time in the Data set. This system is very effective for urban traffic data collection.

Mobile millennium is a traffic information system which is developed in UC Berkeley. (Alexandre et al., 2011). It is traffic flow estimation and forecasting system by using GPS data from drivers running cell phone applications, running car and taxis, and GPS data sources. Mobile Millennium actually is a research project which performs a real-time traffic estimation system that uses the GPS data from probe vehicles and in cellular phones to gather traffic information which receives new data every few minutes, processes it, and distribute it back to the phones in real time.

d. Emerging and Non-Traditional method i) Extrapolation Method In this method travel time data is estimated mainly by determining spot speed by point detection devices. This method can be applied for short roadway segments between detection devices which require high level of accuracy. Spot speeds are typically collected by a number of traffic monitoring devices that are considered intelligent transportation systems (ITS) components. The traffic monitoring devices which can be used effectively for spot speed determinations are listed below:

 Inductance loop detectors;  Piezoelectric sensors;  Active and passive infrared sensors;  Magnetic sensors;  Video tracking and trip line systems;  Doppler microwave;  Passive acoustic sensors; and  Pulse ultrasonic detector.

The most common and widely implemented point detection device is the inductance loop detector. By Loop detectors vehicle flow and occupancy, and vehicle speed can be measured. This system consists of three components:  A loop (preformed or saw-cut),  Loop extension cable and  A detector. Loops cannot directly estimate the speed of vehicle, but using a two-loop speed trap or a single loop detector speed can be measured with the help of an algorithm whose inputs are loop length, vehicle length, time over the detector and numbers of vehicles.

Advantages of loop detector method:  The operation of inductive loop sensors is well understood.

 Properly installed and maintained loop detector performs the best in all weather, all light condition sensors for many applications.  The ILD performs well in both high and low volume traffic.  A Loop sensor satisfies a large variety of applications due to their flexible design.

Disadvantages of loop detector method:  The main demerit of using inductive loop sensors is it include disruption of traffic for installation and repair, these problems are accentuated when loops are installed in poor pavement or in areas where utilities frequently dig up the road bed, resurfacing of roadways and utility repair can also create the need to reinstall these types of sensors.  Sources of loop malfunction such as stuck sensors can produce erroneous data and may lead to inaccurate detection.  Also, wire loops are subject to stresses of traffic and temperature ii) Vehicle signature matching method: In this method travel time is estimated by correlating unique vehicle signatures between sequential observation points. a number of point detectors such as inductance loop detectors, weigh-in motion sensors, video cameras, and laser scanning detectors are very useful in this method.

iii) Platoon matching method By platoon matching method average travel time is estimated by matching vehicle platoons such as the position, distribution of vehicle gaps or unique vehicles. Platoon matching uses point detection devices, such as video cameras, ultrasonic detectors, etc. with correlation methods.

iv) Aerial Surveys Vehicle density or track vehicle movement is measured to estimate travel in this method. Typically fixed-wing aircraft is used for aerial surveys, but

recently use of weather balloons, satellites, and remote controlled gliders is recommended.

As in Dhaka city there is a very high cell phone market penetration (Ching et al., 2012), there are many smartphone users from which acts as GPS enable devices. So for Dhaka city probe vehicle method or travel time Data collection using GPS enabled smart phones is more effective and appropriate way to get huge number of Data set. By using GPS enabled smart phones, GPS data can be extracted easily from internal GPS facility, internet connectivity and the nearby mobile towers. Thus there is a huge opportunity to use this developed technology for collecting travel time data with high accuracy for a complicated road network.

2.3. Visualizing travel time information:

For different road section normally travel time data is visualized through speed- distance graphs, excel sheets etc. but to visualize the data in more. By which the traffic condition for a whole network could not visualize at a time but in this study travel time and speed is estimated for a road network via contour maps and heat maps. The application of these maps can make the visualization a lot much easier and more understandable for specialists and general people than it was before. Now policy makers can identify the most problematic road sections of a network or rank the congested roads priority wise even for a complex road network.

2.3.1 Contour map in transportation planning:

Traffic contour maps are generally considered as pictorial representation of traffic flow condition in terms of time and space. The travel time contour maps provide a general sense of the density of traffic and congestion level in the whole network at a glance.

Considering a central point travel time contour maps are usually plotted in 15minutes interval. (Prassas et al., 2004). An quick assessment of congestion can be estimated by visualizing the contour distances in the map.

Figure 2.1: An example of travel time contour map (Prassas et al., 2004)

In the master‟s study of Kotha, MATLAB is used for the visual representation of the traffic contour maps. MATLAB can be used to develop contours on a digital map in shortest time with high accuracy.

2.3.2 Heat map as congestion map:

At the end of any traffic information system, it is more effective to visualize it and interpret traffic data using algorithms. Color-coded maps are the standard way to visualize and easily understand the traffic condition in various routes at a time. For a same origin-destination pair, drivers can easily pick up the convenient route from many available options. By using color coded map or heat map road users can spot congested areas on the route of Interest.

For example: The Mobile Millennium project, which provides output into a human understandable map by color-coded map system (Alexandre et al., 2011).Here heat map is coded as green for fast, yellow for slower and red for very slow or stopped depending on vehicle speed and overlaid on a map of roads, overlaid maps then visualized as real time congestion map to the traveller.

Figure 2.2: An example output of traffic estimates on the Mobile Millennium visualizer

GPS data from GPS-enabled mobile phone and vehicles is sufficient to construct an accurate velocity map over time and space. By MATLAB programming software from available travel time and distance data of the links of a network color coded heat maps are prepared in very short time.

GIS visualization tool is also important in transportation research. It has capability of efficiently processing large data and developing heat map by using latitude longitude data on the map. (Wu and McLaughlin, 2012)

2.4. Evaluation of route choice:

For a definite O-D pair route choice decision is dynamic process. To choose the shortest possible way for travelling in a certain origin to destination travel time

information is required. It is reported that, most of the travelers made route choice based on their earlier experiences. (Yang et. Al,1993)

Shortest route choice in different times a day and different day a week can be estimated based on shortest path algorithm e.g. Dijkstra's Algorithm. The shortest distance or travel time between two nodes in a network can be determined by several algorithms. These are Dijkstra‟s algorithm, Bellman-Ford algorithm, A* search algorithm, Floyd-Warshall algorithm, Jonshon‟s algorithm and Viterbi algorithm. Among these, Dijkstra‟s algorithm solves for single source shortest path problem to all other nodes with positive weight that was travel time for the study. Dijkstra's Algorithm calculates minimum costs and paths effectively with minimum amount of error. (Dijkstra, l959) Here Dijkstra's Algorithm used for calculating travel time in shortest route for identifying direct node to node travel time. Dijkstra‟s Algorithm is outlined as follows:

In this study Dijkstra's Algorithm is used for evaluating shortest path for a certain O- D pair in different times of a day and different days of weeks. In real time traffic data it can help travelers and drivers to choose the shortest possible route for different destination.

2.5 Other Algorithms for shortest path evaluation:

Another common single source Algorithm for shortest path estimation is Bellman Ford Algorithm solves the single-source shortest-paths problem is solved by Bellman-Ford algorithm in the case in which edge weights may be negative. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is

capable of handling graphs in which some of the edge weights are negative numbers. Negative edge weights are found in various applications of graphs, hence the usefulness of this algorithm. If a graph contains a "negative cycle" (i.e. a cycle whose edges sum to a negative value) that is reachable from the source, then there is no cheapest path: any path can be made cheaper by one more walk around the negative cycle. In such a case, the Bellman–Ford algorithm can detect negative cycles and report their existence. The algorithm calculates shortest paths in bottom-up manner. It first calculates the shortest distances for the shortest paths which have at-most one edge in the path. Then, it calculates shortest paths with atmost 2 edges, and so on. After the ith iteration of outer loop, the shortest paths with at most i edges are calculated.

2.6 Intersection Delay Study:

Delay study is conducted to evaluate performance of a road network system. In general, delay can be defined as the additional time required or time lost to travel between origin and destination. Intersection delay study is to evaluate the performance of intersections in allowing traffic to enter and pass through, or to enter and turn onto another route. Delay is a measure that most directly relates driver‟s experience and it is measure of excess time consumed in traversing the intersection. Delay of intersections can be of many categories. Some the types are stated below:

 Uniform Delay: Uniform delay is the delay based on an assumption of uniform arrivals and stable flow with no individual cycle failures. In this type of delay of a signalized intersection, there is no failing signal cycle, i.e., no vehicles are forced to wait for more than one green phase to be discharged. During every green phase, the departure function catches up with the arrival function. This type of delay is known as Uniform delay

 Random Delay: In this type of delay flow is randomly distributed rather than uniform at isolated intersections which is opposite of uniform delay. At the end of the second and third green intervals, some vehicles are not served (i.e., they must wait for a second green interval to depart the intersection). By the

time the entire period ends, however, the departure function has caught up with the arrival function and there is no residual queue left unserved.

 Overflow Delay: Overflow delay is the additional delay that occurs when the demand or arrival flow rate is higher than the capacity of an individual phase or series of phases. When demand exceeds capacity (v/c >1.0), the delay depends upon the length of time that the condition exists. This type of delay is referred to as Overflow delay.

Chapter 3

Methodology

3.1 Introduction:

This section briefly discusses the overall methodology followed in this research. It describes the study area, the data collection method and characteristics of the data, step-by-step methods that were followed to translate the probe vehicle data into link travel time, and finally, the analysis conducted on the processed data. The overall work-flow diagram of the methodology is presented with Figure 3.1.

Figure 3.1: Work-flow diagram for traffic congestion study

3.2 Study corridors:

The selected study area is mainly in the middle of the south zone that covers mainly five corridors such as Kazi Nazrul Islam Avenue, S Captain Munsur Ali Avenue, Shaheed Tajuddin Ahmed Avenue, DIT Road and Mirpur Road The pictorial view from the Google map of the study area is shown below:

Figure 3.2: Selected study area

Travel time data is collected on 71 nodes and 108 links on the selected Study area shown in Figure 3.3. Distances of the links are estimated from Google map using distance measurement tool. Total length of the links is estimated 70.2 km.

Figure: 3.3. Links and nodes on selected Road network

3.3. Data collection and Pre-Processing

3.3.1 Data Collection

In this study probe vehicle technique is adopted for collecting travel time data. By GPS enable mobile phones and vehicle tracking devices, probe vehicle data (position, travel time) is collected on the selected corridors of Dhaka city road network as explained in Section 3.2. The raw data is collected as a text file format shown below:

rover001,1398745525,23.7270217,90.4225237,-4.199999809265137,1.0,3.9

rover001,1398745530,23.7270222,90.422406,-3.5,2.5,96.0

rover001,1398745535,23.7270222,90.422406,-3.5,2.5,96.0

rover001,1398745540,23.7267648,90.4222955,-5.300000190734863,3.25,13.0

rover001,1398745545,23.7267043,90.4221538,-8.5,2.75,10.0

rover001,1398745550,23.7265906,90.4220458,-8.100000381469727,2.75,8.0

rover001,1398745555,23.7264554,90.422065,-4.599999904632568,2.0,12.0

rover001,1398745560,23.7263648,90.4220316,-5.199999809265137,1.75,14.0

rover001,1398745565,23.7263251,90.4220473,-4.099999904632568,1.5,11.0

rover001,1398745570,23.7262907,90.4219757,-10.399999618530273,1.25,9.0

rover001,1398745575,23.7263086,90.4218732,-10.699999809265137,0.0,8.0

rover001,1398745580,23.726296,90.4218558,-12.0,0.0,12.0

rover001,1398745585,23.7262723,90.4218428,-10.600000381469727,0.0,10.0

rover001,1398745590,23.7262598,90.4218582,-7.400000095367432,0.0,9.0

rover001,1398745595,23.726253,90.4218687,-6.900000095367432,0.0,9.0

Figure 3.4: Collected data format

In this data set first column is ID of data series – representing each device; second, third and fourth columns are unix time, latitude, longitude respectively. Rests of the columns are for altitude, speed and accuracy respectively. With the data collection application of cell phones and GPS-device Data is collected in 10 sec interval.

In this study data in collected for four months in different routes. The analysis is done with total 156375 data point collected for a selected 71 nodes and 108 links of the road network.

3.3.2 Data processing:

The main purpose of the data processing is to go through the raw data to extract the usable Data set which can be used to create a travel-time lookup table that will contain average travel time for different links on the network for different time of day – which will eventually be used to achieve the objectives – preparation of travel time heat map, contour map and evaluating if route choices differ for different origin- destination pairs for different time of day and different day of week. The detailed process is explained step-by-step using Figure 3.1. Hence, the data processing activity can be subdivided into three steps as follows:

 Data importing to MATLAB: At first from the collected data set, column 1,5,6,7 are deleted and further processing is done on rest of the columns of Unix time, latitude and longitude as the travel time look-up does not need rover ID, altitude, speed and accuracy data. The lookup table preparation as well as all other analysis is done using MATLAB- programming software. Hence, new formatted data set then will be imported in MATLAB as a text file.

 Plotting data on Google map: From the huge amount of data set, plotting and observation helps to identify error or to understand which part of the data have to analysis. The following types of plots can be generated:  Time Plot  Histogram  Spectral Plot  Correlation Plot  XY Plot After plotting it is more suitable to observe the data series if there is any distortion or discontinuation in the data set. Here, the time-wise and day-wise GPS (latitude, longitude) data is plotted as (x, y) coordinate, time as z coordinate and superimposed on a Google Map plugin.

After plotting all latitude longitude data as x and y co-ordinate respectively the graph created in shown in Figure below:

Figure 3.5: Latitude and longitude data plotted in Google map

 Selecting the nodes: From this superimposed Google map, 71 nodes are selected for estimating travel time. Latitudes and longitudes of these nodes are listed manually in format of „node number, latitude, longitude‟.

 Measuring distances of the links: Distances from adjacent nodes are measured using Google map manually by measure distance tool and listed in a table in format of „node number 1, node number 2, distance‟.

 Converting Unix time data: MATLAB converts unix time with the following values: 'weeks', 'days', 'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds', and 'nanoseconds'.

In this data collection method, travel time for each location is kept in the form of Unix time. Hence, after importing the data into MATLAB, Unix time is converted into day-month-year (dd-mm-yy) and hour-minute-second (hr- min-sec) format by using the function datestr in MATLAB.

3.4. Preparing Travel-Time Lookup Table:

 Coding data time and day: As this study is related to time varying travel time estimation, the total data set is sorted by using find and sort function in for different day of time and different day of week. Sunday to Thursday was ranked as 1 to 5 which are working day and Friday to Saturday ranked as 6, 7 which are weekend in Bangladesh. The data set at first sorted into 4 groups 8.00.00AM- 11.59.59AM, 12.00.00PM-3.59.59PM, 4.00.00 PM-7.59.59PM, and 8.00.00PM- 10.59.59PM.

 Removing unnecessary data: In the data set, collected data points are on the nodes and also on the links, but to calculate the travel time of the links, only the node‟s travel time data points are needed. So, by providing a tolerance limit (tol = 0.000400) several latitude longitude data of these nodes are listed and the data other than the nodes are removed from total data set by replacing with empty matrix. After removing the data point of links the node data are shown in Figure 3.5.

Figure 3.6: Plot of only node Data

 Sorting time varying travel time in various links: Travel time from one node to another node is calculated as In-travel time for selected links and sorted in the time-wise and day-wise group stated before. For the travel time data for a selected time zone is taken the average value for working day and weekend day separately.

 Sorting data in ascending value of time and calculating travel time Data: By using sortrows function data are sorted in ascending value of Unix time. Difference between the 1stunix time value of a node and next first unix time value of another node is calculated as the travel time value of the link. And only a data point is considered as 1st node point when the date of unix time changes or another new day starts. Example of this calculation is elaborated in the Table 3.1. Unix Time Date Day Hour Latitude Longitude Nodes Remarks 1394790445 14 6 15 23.76469 90.38909 44 1st Unix time Data of node 44 1394790505 14 6 15 23.76471 90.3891 44 -

1394790566 14 6 15 23.76472 90.3891 44 -

1394790686 14 6 15 23.75889 90.38383 61 1stUnix time Data of node 61 1394795396 14 6 17 23.73761 90.40863 28 1stUnix time Data of node 28 1394795456 14 6 17 23.73755 90.40898 28 -

1394795636 14 6 17 23.73133 90.42131 2 1stUnix time Data of node 2 1394866574 15 7 12 23.75414 90.41533 5 1stUnix time Data of node 5 1394866754 15 7 12 23.75857 90.41772 4 1stUnix time Data of node 4 1394866814 15 7 13 23.75893 90.41799 4 -

Table 3.1. Sorted Unix time Data in ascending order

The travel time of the link 44-61 has been calculated by subtracting the value of 1st Unix time Data of node 44 from the 1st Unix time Data of node 61. That is (1394790686-1394790445) = 241 seconds. But as the date changes in row 8th row from 14 to 15, node number 2 and node number 5 did not make a link. So from a new date travel time data has been calculated as 1st point of a new link. Also, if there is any change of date between two consecutive nodes, it does not consider as a link.

 Speed calculation using time and distance: As distance and travel time data are available for the selected links, speed on these links are calculated and listed on the output Table for different time zone in weekend and working day. And also, as the accuracy of the data depends on the proper exposure of the devices to be able to collect data as well as the data collectors‟ accuracy in starting and stopping the device, it needed a careful data cleaning process to extract the usable data out of the raw dataset. If the data collector has forgotten to turn off the device after reaching the destination, the device will be accumulating a lot of data points at the same location. Also, if the GPS does not get good coverage, it will not be able to record location data. That‟s why the Data points some unreasonable Data points are deleted from the Data set after calculating the speed of the links.

3.5. Development of contour map:

 Selecting center for contours: Point number 48 (23.742651, 90.395722, Ruposi Bangla Mor) is selected as center point of the contours. Node number 48 is almost in the middle in the zone of selected study area. And there exists a multiple path network around this point. So it is selected as the center point of the contour maps.

 Use of Dijkstra's Algorithm: MATLAB is matrix-oriented for that it is most efficient for making fast calculations graphs with a very large number of

data. By using MATLAB, Dijkstra's Algorithm shows travel time calculation shortest route with accuracy.

To plot contour map for the study zone, it was necessary to have the travel time from an origin to all other nodes. This was done by Dijkstra‟s algorithm only that, the travel time was treated as „distance‟ or „weight‟ from one node to another. In this study contours are drawn in a certain interval Minimum path or direct paths from the center node to other nodes are found using Dijkstra's Algorithm using MATLAB.

 Drawing contours: The basic syntax for creating contour plots is contour (X,Y,Z, levels) in MATLAB. By specifying a positive integer number contour s can be plotted automatically or a list of contour value have to listed in the levels of arguments. For example: % Create contour plot with 2-D grids, 4 contour levels, black solid contours contour(X, Y, Z, [0.5 1.0 1.2 1.5], 'k'), here contour color are specified to plot in black color. Contours from the center are drawn in MATLAB by using the function tricontour for different time of a day and weekday and weekend day. tricontour is a function that takes latitude and longitude as x and y coordinates respectively to plot the spatial distribution of the nodes and travel time as z coordinates.

The function interpolates by triangulation of the given data to produce more data to plot continuous contour map. Since the resulting plot is a three dimensional plot, the viewing angle is set along z axis so that the travel time (z coordinates) appear as „flat‟ thus producing contour map effect.

It was also necessary to specify the contour interval which was 10 min in this study.

3.6 Development of heat map:

MATLAB takes commands for plotting lines of different colored line for different range of values or same colored line for same value in a plot.

The basic syntax for creating line plots is plt.plot(x,y), where x and y are arrays of the same length that specify the (x; y) pairs that form the line. It is possible to plot the line using a predefined list of color, line styles and markers in specified color for specified value range.

After forming the output table for travel time, time varying heat map for the selected time zones are developed. The table was passed into wgplot function to develop heat map. The function name wgplot stands for weighted graph plot. Since the heat map was a weighted map (velocity as weight) it was necessary to have a function that can plot weighted graph (network).

It takes the network as an adjacency matrix. An adjacency matrix is a representation of a network using n by n matrix. If there is a nonzero value in an element of that matrix, then there exists a connection between that particular row (as node A) and that particular column (as node B). And the value of that element represents „weight‟ which was travel time for this study.

The function then returns the heat map based on a color weightage. If velocity was less that 10 km/hr then it is represented by red link in heat map, green if less than 20 km/hr but greater than 10 km/hr and blue if velocity is greater than 20 km/hr.

3.7 Evaluation and Demonstration:

In this study route choice variation for different time of a day and days of a week for a certain O-D pair has been estimated by using Dijkstra's Algorithm. The minimum path or shortest path estimation for Origin-Destination pair 48(23.742651, 90.395722, Ruposhi Bangla Mor) to 37(23.727629, 90.410457, Shohid Noor Hossain

Square) has been tested for different time slots as states earlier of weekend and weekdays.

3.8. Calculation of Intersection Delay for weekend and week days:

As travel time data is collected for the links of the selected routes, the delay for each junction is also estimated in this research. For calculating intersection delay, at first the data set is rearranged in ascending order as table 3.1. After that, the first unix time of a node to last unix time of the same nodes is subtracted to calculate intersection delay and it is continued to other nodes. This calculation of delay is carried separately for weekend days and weekdays. The process of the calculation is illustrated below:

Date Day Hour Latitude Longitude Nodes Remarks Unix Time 1394795396 14 6 17 23.73761 90.40863 28 1st Unix time Data of node 28 1394795456 14 6 17 23.73755 90.40898 28 Last Unix time Data of node 28 1394902389 15 7 22 23.75687 90.37496 71 1st Unix time Data of node 71 1394902449 15 7 22 23.75652 90.37526 71 - 1394902509 15 7 22 23.75651 90.37533 71 - 1394902569 15 7 22 23.75637 90.37517 71 Last Unix time Data of node 71 1394866754 15 7 12 23.75857 90.41772 4 1stUnix time Data of node 4 1394866814 15 7 13 23.75893 90.41799 4 Last Unix time Data of node 4

Table 3.2. Sorted Unix time Data in ascending order

As table 3.2 shows the unix time of different nodes in accending order of unix time, the intersection delay at node 28 is (last Unix time Data of node 28- 1st Unix time Data of node 28) = 1394795456- 1394795396= 60 seconds. Also the node data is considered as a 1st unix time data of the same node when the date is changed or a new day. Finally average of the delay time of same nodes are listed as the delay for each junctions.

Chapter 4

Data Analysis and Results

In this chapter travel time data collection, route selection, data estimation, visualization and evaluation of the study are briefly demonstrated. In this study by GPS devices travel time data is collected, processed and visualized in different time of a day and different day of week. Finally for a selected O-D pair change in shortest possible route has been estimated by using shortest path algorithm for different times. Analysis on this study is mainly done by MATLAB programming software. Detail of analysis is briefly discussed step by step below:

4.1. Data processing:

The raw data collected from the GPS devices and mobile phones is processed in order to keep only the unix time, latitude and longitude data of any points. As unix time data on the travelled path is collected 10sec interval the data set is arranged in ascending order and the unix time data is converted into Year-Month-Day-Hour- Minute-Second format by using MATLAB. The outcome of the format is shown in Appendix A Section A.1.1.

Latitude-longitude of the nodes and spaces of the links are listed manually using Google map which is listed in Appendix B.

As travel time data is collected in 10 sec interval, the data set is sorted only for the nodes travel time data and the other data is emptied by using null matrix. And a tolerance limit of 0.00400 has given for listing all the data collected on the selected nodes.

After listing the data only for the nodes (Refer to Section A.1.2 in Appendix A) it is arranged in ascending order and only the IN-TIME data is separated in a Table. (Refer to Section A.1.3 in Appendix A). IN-TIME travel time is described as the time when the vehicle entered in the node.

The travel time for one node to another is calculated by subtracting the two adjacent IN-TIMEs of the two adjacent nodes shown in Appendix C section C.1.1.

These travel time data set is sorted in 8 groups depending on time. Four Groups of them sorted into the slots as 8.00.00AM- 11.59.59AM, 12.00.00PM-3.59.59PM, 4.00.00 PM-7.59.59PM, and 8.00.00PM-10.59.59PM on Week Day (Sunday, Monday, Tuesday, Wednesday and Thursday). And another four groups are sorted as 8.00.00AM- 11.59.59AM, 12.00.00PM-3.59.59PM, 4.00.00 PM-7.59.59PM, and 8.00.00PM-10.59.59PMon weekend day (Friday and Saturday). This sorting process is done by MATLAB. (Refer to Section C.1.2 in Appendix C).

Speed of the vehicle is calculated by dividing the link space by link travel time for the eight groups of time slots. (Refer to Section C.1.3 in Appendix C).

4.2. Data analysis:

After sorting the link travel time data and speed data the travel time congestion maps are developed for the selected network. Heat maps and contour maps for the 4 different time slot in Week Day and 4 different time slots in weekend days is developed in order to visualize the time wise variation of congestion in Dhaka city road network.

4.2.1. Development of contour map:

By using MATLAB contour map is plotted for 10 min interval on the selected network on the different time slot for weekend days and Week Days. Point number 48 (23.742651, 90.395722, Ruposhi Bangla Mor) is selected as the center point of the contour. Shortest path algorithm Dijkstra's Algorithm is implemented to draw the contour on shortest possible route on the network and thus the contours with contour interval for 10 minutes is drawn on the selected network. Contour maps for various times of weekdays and weekend are illustrated below:

Figure: 4.1. Contour map for Week Day on 8.00.00AM to 11.59.59AM

Figure 4.1 shows during weekday on 8 am to 12 pm, the road is mostly congested in between 17 (Mogbazar Mor) and 8 (Malibag Railgate) number node where contours are most closely lied in the map. As the center is Ruposhi Bangla Mor, one route takes passenger to hatirjheel link road (node 23) in 30 minutes, where another route takes the passenger to Malibag Raingate (node 8) in 30 minutes.

Figure: 4.2. Contour map for Weekend Day on 8.00.00AM to 11.59.59AM

Figure 4.2 shows during weekend day on 8 am to 12 pm, in which contours are not closed like it is in weekday‟s map. But in weekend also the most closed contours are in the location Mogbazar Mor and Malibag Railgate where contours are most closely lied in the map.

Figure: 4.3. Contour map for Week Day on 12.00.00PM to 3.59.59PM

Figure 4.3 shows during week day on 12 pm to 4 pm, the least congested road can be considered the hatirjheel link road (node 20 to node 23). And also most congested area can be considered between the node number 32 (Outer Circular Road, near Ideal school and collegeMor) and node number 12 (Atish Dipankar Road).

Figure: 4.4. Contour map for Weekend Day on 12.00.00PM to 3.59.59PM

Figure 4.4 shows Contour map of weekend day on 12 pm to 4 pm, in which contours are more closed than it is in weekday‟s map. Mostly congested area is between Node number 47 (Kazi Nazrul Islam Avenue, near Pan pacific Sonargaon Mor) to node number 20 (Panthapath-Tejgaon link road) in this time.

Figure: 4.5. Contour map for Week Day on 4.00.00PM to 7.59.59PM

Figure 4.5 shows Contour map of week day on 4 pm to 8 pm, where contour gaps are less than before this time range. Mostly congested area is around Mouchak Mor (node number 16).

Figure: 4.6. Contour map for Weekend Day on 4.00.00PM to 7.59.59PM

Figure 4.6 shows Contour map of weekend day on 4 pm to 8 pm, where gap between each contour line is more than the weekday in this time range, though Malibag Railgate (node 8) and Mogbazar Mor ( node 17) shows more congested than others.

Figure: 4.7. Contour map for Week Day on 8.00.00PM to 11.59.59PM Figure 4.7 shows Contour map during week day on 8 pm to 12 pm, the contour line gaps are more on the east side of the study zone. But between the zone node number 45 (Farmgate Bus Stop) to node number 61 (Khamar Bari Gol Chottor), it shows more congested than other in this time range.

Figure: 4.8. Contour map for Weekend Day on 8.00.00PM to 10.59.59PM

Figure 4.8 shows a contour map during weekend day on 8 pm to 11 pm, which shows maximum congestion in the east zone, from Atish Dipankar Road to DIT road and least congestion in Motijheel area.

4.2.1. Development of heat map: MATLAB takes commands for plotting lines of different colored line for different range of values or same colored line for same value in a plot. By these phenomena

heat map is created for these different time slots and color is predefined for different speed range. Here for 0-10 km/hr. speed range color is defined as RED, for 10-20 km/hr. range speed color is defined as GREEN, and for greater than 20 km/hr. speed color is defined as BLUE. Heat maps for Week Days and weekend days are illustrated below:

Figure: 4.9. Heat map for Week Day on 8.00.00AM to 11.59.59AM

Figure 4.9 shows the heat map of week day on 8am to 12 pm. Maximum area in the west zone of the study area has vehicular speed less than 10 km/hr. Only 3 links e.g. Bir Uttam Ziaur Rahman Road, a portion Rampura and Dilkhusa Road shows blue coloured.

Figure: 4.10. Heat map for Weekend Day on 8.00.00AM to 11.59.59AM

Figure 4.10 shows the heat map during weekend day on 8am to 12 pm. In this tme range during weekend day, maximum road are coloured as green line that indicates vehicle has flow speed of 10 km/hr to 20 km/hr.

Figure: 4.11. Heat map for Week Day on 12.00.00PM to 3.59.59PM Figure 4.11 shows heat map of week day during 12 pm to 4 pm, in which red coloured lines are less than it is in previous time slot though mostly vehicular speed is less than 10 km/hr and 20 km/hr.

Figure: 4.12. Heat map for Weekend Day on 12.00.00PM to 3.59.59PM

Figure 4.12 shows heat map of weekend day during 12 pm to 4 pm, in which green coloured lines are more than it is in week day, though mostly vehicular speed is almost same as weekday in this time slot.

Figure: 4.13. Heat map for Week Day on 4.00.00PM to 7.59.59PM

Figure 4.13 shows heat map of week day during 4 pm to 8 pm, vehicular speed is mostly between 10km/hr to 20 km/hr also there are many roads which have vehicular speed of less than 10 km/hr.

Figure: 4.14. Heat map for Weekend Day on 4.00.00PM to 7.59.59PM Figure 4.14 shows heat map of weekend day during 4 pm to 8 pm, in which Motijheel and Ramna area shows some blue line which has vehicular speed greater than 20 km/hr. other roads are mostly red and green coloured in scatter pattern.

Figure: 4.15. Heat map for Week Day on 8.00.00PM to 11.59.59PM

Figure 4.15 shows heat map of week day during 8 pm to 12 pm, in which there are less red coloured line than the time before 8pm on weekday.

Figure: 4.16. Heat map for Weekend Day on 8.00.00PM to 11.59.59PM

Figure 4.16 shows heat map of weekend day during 8 pm to 12 pm, there are surprisingly more red line than it is in weekday and time before 8 pm on weekend day.

4.3 Evaluation of the congestion maps: In this study, shortest possible route for a certain Origin Destination pair in different day of a week and different time of a day is investigated by using Dijkstra's Algorithm. Change in shortest route for O-D pair 48(23.742651, 90.395722, Ruposhi Bangla Mor) to 37(23.727629, 90.410457,Shohid Noor Hossain Square) is calculated by Dijkstra's Algorithm.

As it is shown in Table 4.2 that shortest possible route for O-D pair 48-37 is not same for different time of a day in Week Day. But at 8.00.00PM-10.59.59PM travel time for shortest possible route is 7.46 minutes which is much less than other times of a day.

Day Time Slot Time Route (Minutes)

Week 8.00.00AM- 11.59.59AM 15.78 48 49 50 39 38 68 37 day 12.00.00PM-3.59.59PM 14.05 48 49 50 51 53 68 37

4.00.00 PM-7.59.59PM 16.01 48 49 40 39 38 29 37

8.00.00PM-10.59.59PM 7.46 48 49 40 39 38 68 37

Table 4.1: Shortest Path and time for 48-37 O-D pair on Week Day

Figure 4.17: Shortest Path and time for 48-37 O-D pair on Week Day ( 8.00.00 AM to 11.59.59 AM)

Figure 4.18: Shortest Path and time for 48-37 O-D pair on Week Day ( 12.00.00 PM to 3.59.59 PM)

Figure 4.19: Shortest Path and time for 48-37 O-D pair on Week Day ( 4.00.00 PM to 7.59.59 PM)

Figure 4.20: Shortest Path and time for 48-37 O-D pair on Week Day ( 8.00.00 PM to 11.59.59 PM)

In Table 4.2, the shortest possible route for O-D pair 48-37 is shown for weekend days at different time slots. It is observed that for weekend day the shortest route is same for different time of a day in weekend day except for time slot 8.00.00PM- 11.59.59 PM. At 8.00.00PM- 11.59.59 PM minimum travel time on this O-D pair is found as 7.63 minutes and varies slightly on the shortest route pattern than other times.

Day Time Slot Time Route (Minutes)

Weekend 8.00.00AM- 11.59.59AM 8.11 48 49 50 39 38 29 37 Day 12.00.00PM-3.59.59PM 16.33 48 49 50 39 38 29 37

4.00.00 PM-7.59.59PM 9.55 48 49 50 39 38 29 37

8.00.00PM-10.59.59PM 7.63 48 49 40 39 38 29 37

Table 4.2: Shortest Path and time for 48-37 O-D pair on weekend day

Figure 4.21: Shortest Path and time for 48-37 O-D pair on Weekend Day ( 8.00.00 AM to 11.59.59 AM)

Figure 4.22: Shortest Path and time for 48-37 O-D pair on Weekend Day ( 12.00.00 PM to 3.59.59 PM)

Figure 4.23: Shortest Path and time for 48-37 O-D pair on Weekend Day ( 4.00.00 PM to 7.59.59 PM)

Figure 4.24: Shortest Path and time for 48-37 O-D pair on Weekend Day ( 8.00.00 PM to 10.59.59 PM)

4.4 Evaluation of intersection delay: Average Delay at different intersections or nodes in the selected corridors of this study is calculated for both weekdays and weekends. Sample Table for average intersection delay is shown below: Weekend day Week day

Node Intersection Name Intersection Node Intersection Name Intersection number Delay (sec) number Delay (sec) 44 Bijoy Sarani 18.9 Kamlapur Nursary 34 Mor 22.5

61 Khamar Bari Gol 117 Ruposhi Bangla Chattar 49 Mor 30.1 28 Kakrail Mor 16.9 Bangla Academi 52 Mor 2776 2 Arambag Mor 7.7 45 Farmgate 3815.2 5 Chowdhury Para 6.4 and DIT Road Junction 46 Kawran Bazar Mor 23.3 4 Mirbag and DIT 66.7 Junction of Road Junction Shaheed Tajuddin Ahmed Avenue & Panthapath 20 Tejgaon Link Road 480.5 43 Mohakhali 15 Intersection 23 Hatir Jheel Link 0 49 Ruposhi Bangla 16.8 Mor 47 Bangla Motor 55 50 Paribag Junction 27.2 1 Santinagar Mor 0 51 TSC Mor 25 48 Shahbag Junction 17.6 71 Rapa Plaza Mor 80.6 57 Russsel Square 3.75

Table 4.3. Average Intersection delay in week days and weekends

Chapter 5 Conclusion and Recommendations

The main focus of this study was to determine congestion variability on Dhaka city road network in different time. Congestion maps were made to observe the time dependent variation of the congestion. The study was also designed to investigate how shortest possible route for different times of different days changes for a certain O-D pair. Estimation for intersection delay is also made for some selected nodes in this study.  From this study on Dhaka city road network, generated heat maps and contour maps does not show a huge variety on travel time for weekdays and weekends. Also it seems there is a significant change in congestion from 1st slot (8.00.00AM- 11.59.59AM) to 2nd slot(12.00.00PM-3.59.59PM) , 3rd (4.00.00 PM-7.59.59PM) and 4th slot (8.00.00PM-10.59.59PM) on weekday. And for weekend maps shows congestion criteria is mostly haphazard pattern, though most of the congestion occurs on 2nd slot (12.00.00PM-3.59.59PM) and 4th slot (8.00.00PM-10.59.59PM) on weekend.  Also, an attempt has been made to evaluate the shortest path in various times for a certain O-D pair. In this analysis it is found that for this certain O-D pair shortest possible route is same for weekend but in different time of a working day shortest routes varies significantly. It shows that if proper information is available in different days of a week or different times of a day, route choice for a traveler in real time can be varied depending on congestion on the routes. The shortest path estimation for the O-D pair 48(23.742651, 90.395722, Ruposhi Bangla Mor) to 37(23.727629, 90.410457, Shohid Noor Hossain Square) is done by Dijkstra's Algorithm, which shows a shortest path for this pair varies in weekday but is almost same for weekend on this pair.

In conclusion, using the model developed in this study, transport policies should be tested and upgraded for Dhaka city. Although the data set is relatively small and the study implemented on a selected network, it shows the viability of utilizing choice

modeling methodologies in the estimation of route choice behavior from cell phone GPS data. In the future, the proposed algorithm and route choice modeling can be used to generate more path observations from cell phone GPS data, and develop real time congestion maps and implemented to change driver‟s route choice decisions.

Recommendations:  This study developed a most efficient travel time collection technique for Dhaka city that can be used to get real-time travel time data and process it to congestion map. Thus real-time traffic congestion map can help the road users for route-choice in most efficient manner. This experiment is viewed as realistic in the near future depending on the increasing penetration of GPS- enabled cellular devices in Dhaka city. It is expected that GPS-enabled cell phones will penetrate the market rapidly in the near future, and the quality of measurements will increase with the evolution of GPS technology, thus opening new opportunities for smartphone-based monitoring systems in transportation sector.  Since few techniques have been developed to reveal how travel time varies in different time in Dhaka city Road network, this study developed how congestion on this road network is different in different time through visualization of travel time by heat maps and contour maps. It is expected that the methodology developed here will be applicable to analyze congestion patterns in many other congested cities around the world. The MATLAB based congestion maps developing system can use as a model to generate real time congestion determination which is developed in this study. To apply this model to acquire real time traffic congestion maps maximum numbers of vehicles have to be GPS enabled vehicles in Dhaka city. Also policy makers and general road users can use this system to identify the problems or bottlenecks on the network and maintain traffic flow most effectively  In this study only the congestion for a selected segment of Dhaka city road network is analyzed. This study can be further extended by using this analysis model on the whole Dhaka city road network. And also other divisional

metropolitan city in Bangladesh. So it is recommended that, a further research to conduct for a „real‟ environment with a sufficiently large network at various time periods, under various traffic conditions withvarious types of road characteristics.  The current research focuses on some motorized vehicle such as car, taxi-cab, micro-bus etc. further study is recommended for other vehicles such as motor cycle, now a days which has become a popular mode of transportation in Dhaka city, for non-motorized vehicles like bi-cycle (Hood et al., 2011) and for public transportation like bus transit which is the only available organized mass public transport system in Dhaka.  As this study in conducted on a relatively small amount of data set, it cannot be treated as a generalization for traffic network conditions but for an increasing amount of the sample size route choice estimation errors can be reduced. And for a whole road network and a large data set this model developed in this study can show efficient and sustainable forms of transportation guideline for general people, transportation specialists and policy makers.

References

Jensen, A. F. & Larsen, T. V. (2010) Travel-Time Estimation in Road Networks Using GPS Data. The Project Library, Aalborg University.

Chen, J., Newman,J. &Bierlaire, M., (2009) Modeling Route Choice Behavior From Smart-phone GPS data. Transport and Mobility Laboratory, EcolePolytechniqueFédérale de Lausanne, CH-1015 Lausanne, Switzerland.

LIN, H. & ZITO, R., (2005), A review of travel-time prediction in transport and logistics, Proceedings of the Eastern Asia Society for Transportation Studies, Vol. 5, pp. 1433 – 1448.

PUANGPRAKHON, P. & NARUPITI, S., (2013) Travel Time Estimation by Running Speed and Stopped Delay Method on Urban Road Using Probe Vehicle Data, Proceedings of the Eastern Asia Society for Transportation Studies, Vol.9.

Ching, A. et.al., (2012), A User-Flocksourced Bus Experiment in Dhaka: New Data Collection Technique with Smartphones. MIT Master's Thesis, http://web.mit.edu/czegras/www/Flocksource_JUT.pdf.

Elena S. Prassas, Roger P. Roess&William R. McShane.(2004).Traffic Engineering. New Jersey. Prentice Hall.

Wu, S. McLaughlin, S. (2012) Creating a Heatmap Visualization of 150 Million GPS Points on Roadway Maps via SAS®. In the proceeding of SESUG 2012. Paper RI-14

Sanwal, K., Walrand, J. (1995), “Vehicles as Probes”, California PATH Working Paper 25 UCB-ITS-PWP-95-11, ISSN:1055-1417, University of California, Berkeley.

Mobile Millennium Project.http://traffic.berkeley.edu.

Bayen, A. M., Butler, J. and Patire, A. D., (2011), Mobile Millennium Final Report. CCIT Research Report UCB-ITS-CWP-2011California Center For Innovative Transportation Institute Of Transportation Studies University Of

California, Berkeley. Report prepared for California Department of Transportation, Sacramento, United States.

Mazare, P-E, Claudel,C.G and Bayen, A.M. (2011). Analytical and grid-free solutions to the Lighthill-Whitham-Richards traffic flow model. Submitted to Transportation Research,Department of Civil and Environmental Engineering, University of California at Berkeley, Berkeley California, USA.

Kotha, P., (2003), A Methodology for Deriving Performance Measures from Spatio-Temporal Traffic Contour Maps using Digital Image.Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the Requirements for the degree of Master of Science in Civil Engineering.

Fosgerau, M. et. al., (2008), Travel time variability Definition and valuation,DTU Transport bygningstorvet 116 Vest 2800 Kgs. Lyngby, ISSN: 1600-9592 (Printed version), ISBN: 978-87-7327-174-2 (Printed version), ISSN: 1601-9458 (Electronic version), ISBN: 978-87-7327-175-9 (Electronic version).

Vanajakshi, L.D., (2004), Estimation and prediction of travel time from loop detector data for intelligent transportation systems applications. Office of Graduate Studies of Texas A&M University,

Office of Traffic and Safety.(2005). Use of Intelligent Transportation Systems in Work Zones. Maryland: Maryland State Highway Administration. http://www.marylandroads.com/OOTS/10ITSinWorkzones.pdf

Mathew, T. V., (2014), Transportation system Engineering book, IIT Bombay, chapter no. 11.

Yang, H. et.al., (1993). Exploration of route choice behavior with advanced traveler information using neural network concepts. Transportation, Vol. 20, pp. 199-223.

Dijkstra, E. W. (1959). "A note on two problems in connexion with graphs". NumerischeMathematik Vol.1 P. 269–271

Hood, J., Elizabeth S., and Billy C.,(2011). “A GPS-Based Bicycle Route Choice Model For San Francisco, California.” Transportation Letters-The International

Journal of Transportation Research 3, no. 1 (January): 63–75. doi:10.3328/TL.2011.03.01.63-75.

Appendices

APPENDIX A: DATA FORMATION –CONVERSION OF UNIX TIME This appendix contains documentation for the formatted data set of collected travel times. Chapter 3 of the handbook describes the data formation method that can utilize in MATLAB software for further processing. In the Table below is sample of whole data set that converted into usable Data set for MATLAB.

Appendix A Section A.1.1.: SampleData set after converting Unix time in Year- Month-Day-Hour-Minute-Second format

Unix time Year Month Date Day Hour Minute Second Latitude Longitude 1394779376 2014 3 14 6 12 42 56 23.86824 90.39886 1394779381 2014 3 14 6 12 43 1 23.86824 90.39886 1394779386 2014 3 14 6 12 43 6 23.86824 90.39886 1394779391 2014 3 14 6 12 43 11 23.86824 90.39886 1394779396 2014 3 14 6 12 43 16 23.86824 90.39886 1394779401 2014 3 14 6 12 43 21 23.86824 90.39886 1394779406 2014 3 14 6 12 43 26 23.86824 90.39885 1394779411 2014 3 14 6 12 43 31 23.86824 90.39885 1394779416 2014 3 14 6 12 43 36 23.86824 90.39885 1394779421 2014 3 14 6 12 43 41 23.86825 90.39885 1394779426 2014 3 14 6 12 43 46 23.86825 90.39885 1394779431 2014 3 14 6 12 43 51 23.86825 90.39885 1394779437 2014 3 14 6 12 43 57 23.86825 90.39885 1394779442 2014 3 14 6 12 44 2 23.86825 90.39885 1394779447 2014 3 14 6 12 44 7 23.86825 90.39885 1394779452 2014 3 14 6 12 44 12 23.86825 90.39885 1394779457 2014 3 14 6 12 44 17 23.86825 90.39885 1394779462 2014 3 14 6 12 44 22 23.86825 90.39885 1394779467 2014 3 14 6 12 44 27 23.86825 90.39885 1394779472 2014 3 14 6 12 44 32 23.86825 90.39885 1394779477 2014 3 14 6 12 44 37 23.86825 90.39885 1394779482 2014 3 14 6 12 44 42 23.86825 90.39885 1394779487 2014 3 14 6 12 44 47 23.86825 90.39885 1394779492 2014 3 14 6 12 44 52 23.86825 90.39885 1394779497 2014 3 14 6 12 44 57 23.86826 90.39885 1394779502 2014 3 14 6 12 45 2 23.86826 90.39885 1394779507 2014 3 14 6 12 45 7 23.86826 90.39885 1394779512 2014 3 14 6 12 45 12 23.86826 90.39885 1394779517 2014 3 14 6 12 45 17 23.86826 90.39885 1394779522 2014 3 14 6 12 45 22 23.86826 90.39885 1394779527 2014 3 14 6 12 45 27 23.86826 90.39885 1394779532 2014 3 14 6 12 45 32 23.86826 90.39885

Appendix A Section A.1.2.: Sample Data set after sorting only for node Data for weekend days and weekdays a) Sample Data set after sorting only for node Data for weekend days

Unix Time Date Day Hour Latitude Longitude Node 1394790445 14 6 15 23.76469 90.38909 44 1394790505 14 6 15 23.76471 90.3891 44 1394790566 14 6 15 23.76472 90.3891 44 1394790686 14 6 15 23.75889 90.38383 61 1394795396 14 6 17 23.73761 90.40863 28 1394795456 14 6 17 23.73755 90.40898 28 1394795636 14 6 17 23.73133 90.42131 2 1394866574 15 7 12 23.75414 90.41533 5 1394866754 15 7 12 23.75857 90.41772 4 1394866814 15 7 13 23.75893 90.41799 4 1394877854 15 7 16 23.77815 90.39808 43 1394878875 15 7 16 23.7416 90.39614 49 1394878996 15 7 16 23.73834 90.39605 50 1394882834 15 7 17 23.73222 90.39528 51 1394883095 15 7 17 23.73801 90.39576 50 1394883155 15 7 17 23.74127 90.39593 49 1394884660 15 7 17 23.77819 90.39758 43 1394884720 15 7 17 23.77821 90.39761 43 1394901942 15 7 22 23.76463 90.38914 44 1394902182 15 7 22 23.75869 90.38394 61 1394902389 15 7 22 23.75687 90.37496 71 1394902449 15 7 22 23.75652 90.37526 71 1394902509 15 7 22 23.75651 90.37533 71 1394902569 15 7 22 23.75637 90.37517 71 1394902989 15 7 23 23.73934 90.38338 56 1394903469 15 7 23 23.73541 90.41739 26 1395400544 21 6 17 23.73023 90.41042 29 1395407481 21 6 19 23.73242 90.385 65 1395411328 21 6 20 23.75642 90.37524 71 1395416272 21 6 21 23.73043 90.41006 29 1395416332 21 6 21 23.73035 90.40983 29 1395416452 21 6 21 23.7304 90.4151 30 1395416632 21 6 21 23.7314 90.42111 2

b) Sample Data set after sorting only for node Data for weekdays

Unix Time Date Day Hour Latitude Longitude Node 1394938434 16 1 8 23.72908 90.42548 34

1394945485 16 1 10 23.74194 90.39573 49 1394951169 16 1 12 23.72849 90.39544 52 1394956721 16 1 13 23.72863 90.39567 52 1394964200 16 1 16 23.74171 90.39608 49 1394964746 16 1 16 23.75857 90.39003 45 1394977190 16 1 19 23.75876 90.39 45 1394977370 16 1 19 23.75036 90.39345 46 1394986432 16 1 22 23.75348 90.40042 20 1394986493 16 1 22 23.7536 90.40041 20 1394987515 16 1 22 23.77051 90.42461 23 1395074108 17 2 22 23.75826 90.39014 45 1395074288 17 2 22 23.75025 90.39328 46 1395074348 17 2 22 23.74589 90.39483 47 1395074588 17 2 22 23.74162 90.41192 1 1395116848 18 3 10 23.74136 90.39633 49 1395116908 18 3 10 23.74151 90.3963 49 1395116968 18 3 10 23.74238 90.3957 48 1395117749 18 3 10 23.75083 90.3784 57 1395119730 18 3 11 23.75677 90.37509 71 1395128640 18 3 13 23.78102 90.42577 69 1395128880 18 3 13 23.76977 90.42443 23 1395128940 18 3 13 23.76797 90.4234 3 1395129180 18 3 13 23.75881 90.41805 4 1395129315 18 3 13 23.75458 90.41576 5 1395129375 18 3 13 23.75225 90.4169 6 1395129555 18 3 13 23.74418 90.42662 10 1395129675 18 3 14 23.7364 90.42865 12 1395129795 18 3 14 23.72946 90.42862 13 1395135266 18 3 15 23.7318 90.42078 2 1395135446 18 3 15 23.73713 90.41072 27 1395135626 18 3 15 23.73012 90.40512 38 1395135686 18 3 15 23.72838 90.40416 68 1395135746 18 3 15 23.72839 90.40413 68 1395135806 18 3 15 23.72794 90.4001 53 1395142340 18 3 17 23.72813 90.40022 53 1395142460 18 3 17 23.73002 90.40497 38 1395142706 18 3 17 23.73026 90.40988 29 1395142766 18 3 17 23.73025 90.41051 29 1395143247 18 3 17 23.73029 90.41479 30

Appendix A Section A.1.3.: Sample Data set after sorting only for IN-TIME node Data for weekend days and weekdays a) Sample Data set after sorting only for IN-TIME node Data for weekend days

Unix Time Date Day Hour Latitude Longitude Node 1394790445 14 6 15 23.76469 90.38909 44 1394790686 14 6 15 23.75889 90.38383 61 1394795396 14 6 17 23.73761 90.40863 28 1394795636 14 6 17 23.73133 90.42131 2 1394866574 15 7 12 23.75414 90.41533 5 1394866754 15 7 12 23.75857 90.41772 4 1394877854 15 7 16 23.77815 90.39808 43 1394878875 15 7 16 23.7416 90.39614 49 1394878996 15 7 16 23.73834 90.39605 50 1394882834 15 7 17 23.73222 90.39528 51 1394883095 15 7 17 23.73801 90.39576 50 1394883155 15 7 17 23.74127 90.39593 49 1394884660 15 7 17 23.77819 90.39758 43 1394901942 15 7 22 23.76463 90.38914 44 1394902182 15 7 22 23.75869 90.38394 61 1394902389 15 7 22 23.75687 90.37496 71 1394902989 15 7 23 23.73934 90.38338 56 1394903469 15 7 23 23.73541 90.41739 26 1395400544 21 6 17 23.73023 90.41042 29 1395407481 21 6 19 23.73242 90.385 65 1395411328 21 6 20 23.75642 90.37524 71 1395416272 21 6 21 23.73043 90.41006 29 1395416452 21 6 21 23.7304 90.4151 30

b) Sample Data set after sorting only for IN-TIME node Data for weekdays

Unix Time Date Day Hours Latitude Longitude Node 1394938434 16 1 8 23.72908 90.42548 34 1394945485 16 1 10 23.74194 90.39573 49 1394951169 16 1 12 23.72849 90.39544 52 1394964200 16 1 16 23.74171 90.39608 49 1394964746 16 1 16 23.75857 90.39003 45 1394977370 16 1 19 23.75036 90.39345 46 1394986432 16 1 22 23.75348 90.40042 20 1394987515 16 1 22 23.77051 90.42461 23 1395074108 17 2 22 23.75826 90.39014 45 1395074288 17 2 22 23.75025 90.39328 46 1395074348 17 2 22 23.74589 90.39483 47

1395074588 17 2 22 23.74162 90.41192 1 1395116848 18 3 10 23.74136 90.39633 49 1395116968 18 3 10 23.74238 90.3957 48 1395117749 18 3 10 23.75083 90.3784 57 1395119730 18 3 11 23.75677 90.37509 71 1395128640 18 3 13 23.78102 90.42577 69 1395128880 18 3 13 23.76977 90.42443 23 1395128940 18 3 13 23.76797 90.4234 3 1395129180 18 3 13 23.75881 90.41805 4 1395129315 18 3 13 23.75458 90.41576 5 1395129375 18 3 13 23.75225 90.4169 6 1395129555 18 3 13 23.74418 90.42662 10 1395129675 18 3 14 23.7364 90.42865 12 1395129795 18 3 14 23.72946 90.42862 13 1395135266 18 3 15 23.7318 90.42078 2 1395135446 18 3 15 23.73713 90.41072 27 1395135626 18 3 15 23.73012 90.40512 38 1395135686 18 3 15 23.72838 90.40416 68 1395135806 18 3 15 23.72794 90.4001 53 1395142460 18 3 17 23.73002 90.40497 38 1395142706 18 3 17 23.73026 90.40988 29 1395143247 18 3 17 23.73029 90.41479 30

APPENDIX B: DETAILS OF NODES AND LINKS This appendix provides the latitude longitude of selected node‟s number, latitude and longitude. Appendix B Section B.1.1.:Node‟s Number, Latitude and Longitude Node Latitude Longitude 1 23.74157 90.41184 2 23.73142 90.42093 3 23.76776 90.42305 4 23.75885 90.41787 5 23.75453 90.41562 6 23.75212 90.41693 7 23.74951 90.41602 8 23.74993 90.41279 9 23.74507 90.4264 10 23.7443 90.42662 11 23.73986 90.42755 12 23.73634 90.4285 13 23.72968 90.4286 14 23.72507 90.42871 15 23.72164 90.42158 16 23.74578 90.41197 17 23.74887 90.40794 18 23.74866 90.4037 19 23.7502 90.40272 20 23.7534 90.40076 21 23.76043 90.41048 22 23.73998 90.41991 23 23.77016 90.42447 24 23.74178 90.40574 25 23.72242 90.42878 26 23.7351 90.41707 27 23.73717 90.41036 28 23.73751 90.4089 29 23.73012 90.4102 30 23.7303 90.41514 31 23.72669 90.42156 32 23.73654 90.42327 33 23.73251 90.42516 34 23.72925 90.42536 35 23.72765 90.41507 36 23.7249 90.41198 37 23.72763 90.41046 38 23.72985 90.40509

39 23.7337 90.40372 40 23.73746 90.40468 41 23.74521 90.40416 42 23.72299 90.41185 43 23.77852 90.39775 44 23.7645 90.38881 45 23.75853 90.38988 46 23.75003 90.39323 47 23.74593 90.39458 48 23.74265 90.39572 49 23.74159 90.39611 50 23.73825 90.39589 51 23.73261 90.39556 52 23.72879 90.39574 53 23.72803 90.40022 54 23.72755 90.38951 55 23.73866 90.39086 56 23.73915 90.38332 57 23.75121 90.37827 58 23.75106 90.38707 59 23.75816 90.37437 60 23.75291 90.36978 61 23.75875 90.3837 62 23.76507 90.38319 63 23.77051 90.38247 64 23.77664 90.38062 65 23.7325 90.38501 66 23.73259 90.38704 67 23.72232 90.41591 68 23.72828 90.40415 69 23.78078 90.42559 70 23.74593 90.39256 71 23.75647 90.37518

Appendix B Section B.1.2. :Length of the links

Node 1 Node 2 Length (ft.) 69 23 3951.93 23 3 1298.46 3 4 3363.11 4 5 1846.01 5 6 1045.42 6 7 1035.75 7 8 1065.5 7 9 4022.17 9 10 316.33 10 11 1783.59 11 12 1381.91 12 13 2460.81 13 14 1670.17 14 25 1031.98 25 15 2446.75 15 31 1780.58 8 16 1538.33 16 17 1828.56 17 18 1466.66 18 19 642.67 19 20 724.2 20 21 4791.1 22 10 2881.58 22 26 2038.38 26 27 2393.58 27 28 599.96

27 29 2595.08 29 30 1686.4 30 31 2632.87 22 32 1670.2 32 33 1643.51 33 34 1163.35 30 35 1004.91 36 35 1818.49 36 37 1435.23 38 29 1642.62 38 39 1400.21 39 40 1476.28 36 42 647.53 43 20 9107.48 43 44 6759 44 45 2052.01 45 46 3068.21 46 47 1470.55 47 48 1203.49 48 49 464.16 49 50 1210.76 50 51 1996.06 51 52 1394.96 52 53 1689.16 52 54 2241.91 50 55 1622.45 56 65 2535.47 56 57 4572.91

57 58 3420.24 71 59 792.7 60 71 2226.91 45 61 3107.61 61 62 2382.91 62 63 2060.03 63 64 2294.83 65 66 612.77 55 66 2600.28 42 67 1644.48 53 68 1264.2 47 70 692 57 71 2079.97 21 3 6133.28 15 31 1820.42 31 34 1604.68 31 35 2287.33 37 29 887.14 18 41 1327.53 56 58 4151.25 59 61 3237.04 44 62 1862.87 54 66 2163.93 38 68 795.03 46 58 2053.28 58 70 3088.44 1 24 2066.59 1 28 1783.78

2 33 1468.29 2 31 1895.66 2 26 1842.63 5 8 1942.55 4 21 2675.33 21 17 4576.35 17 41 1940.88 26 30 2008.48 14 34 2464.84 15 67 1911.39 46 20 2821.39 18 47 3176.68 48 41 2870.22 40 49 3455.93 50 39 3401.85 45 58 3043.36 55 70 2757.75 46 70 1467.32 55 56 2480.72 37 68 2206.85 51 66 2898.11 51 53 2332.35 16 22 3406.7 41 24 1388.03 24 40 1712.98 28 40 1320.98 8 1 3631 17 1 4043

40 49 3455 29 30 1640 30 31 2460 29 37 900 59 71 900 36 42 900 7 17 4552 1 17 4119 22 9 3192 1 22 3658 31 34 1655 9 12 3337 31 34 1604 41 8 5210 51 53 2332 18 41 1327 41 24 1388 24 40 1712 37 68 2206 51 53 2332 50 51 1996 49 50 1210 50 39 3401 50 51 1996 49 50 1210 37 68 2206 18 47 3176 48 41 2870

24 40 1712 18 41 1327 48 41 2870 41 24 1388 24 40 1712 50 39 3401 49 50 1210 50 55 1622 38 39 1400 39 40 1476 41 24 1388.03 24 40 1712.98 37 68 2206.85 37 29 887.14 36 37 1435.23 38 68 795.03 38 39 1400.21 39 40 1476.28 1 28 1783.78 16 17 1828.56 17 1 4043 18 41 1327.53 48 41 2870.22 18 19 642.67 18 47 3176.68

APPENDIX C: ESTIMATED TRAVEL TIME AND SPEED DATA OF VARIOUS LINKS

This appendix provides results for travel time and speed estimation for the selected links of the network for various times of weekdays and weekends.

Appendix C Section C.1.1.: Travel time of the links for weekdays and weekend days.

a) Travel time of the links for weekdays

Origin Destination Travel Unix time of Origin time(sec) 44 61 241 1394790445 61 28 4710 1394790686 28 2 240 1394795396 5 4 180 1394866574 4 43 11100 1394866754 43 49 1021 1394877854 49 50 121 1394878875 50 51 3838 1394878996 51 50 261 1394882834 50 49 60 1394883095 49 43 1505 1394883155 43 44 17282 1394884660 44 61 240 1394901942 61 71 207 1394902182 71 56 600 1394902389 56 26 480 1394902989 29 65 6937 1395400544 65 71 3847 1395407481 71 29 4944 1395411328 29 30 180 1395416272

30 2 180 1395416452 30 38 360 1396687273 38 39 60 1396687633 39 49 781 1396687693 49 48 60 1396688474 48 47 420 1396688534 47 46 240 1396688954 46 61 14787 1396689194 61 59 120 1396703981 59 57 361 1396704101 57 56 2387 1396704462 56 55 720 1396706849 55 29 723 1396707569 29 68 5264 1396708292 68 27 180 1396713556 27 2 180 1396713736 31 2 124 1397301798 2 26 121 1397301922 26 27 124 1397302043 27 28 56 1397302167 28 40 245 1397302223 40 49 236 1397302468 49 48 110 1397302704 48 47 140 1397302814 47 46 242 1397302954 46 45 638 1397303196 45 61 822 1397303834 61 59 111 1397304656

59 71 30 1397304767 71 60 571 1397304797 60 57 2977 1397305368 57 71 6099 1397308345 71 59 227 1397314444 59 61 183 1397314671 61 45 1755 1397314854 45 46 511 1397316609 46 47 291 1397317120 47 48 50 1397317411 48 49 10 1397317461 49 40 111 1397317471 40 28 40 1397317582 28 27 24 1397317622 27 26 76 1397317646 26 2 75 1397317722 31 26 181 1397886883 26 27 180 1397887064 27 40 181 1397887244 40 47 240 1397887425 47 46 60 1397887665 46 26 5499 1397887725 26 31 4452 1397893224 31 2 80 1397897676 2 32 256 1397897756 32 22 65 1397898012 22 10 175 1397898077 10 9 10 1397898252

9 7 115 1397898262 7 6 56 1397898377 6 5 51 1397898433 5 4 80 1397898484 4 43 5709 1397898564 43 44 511 1397904273 44 45 90 1397904784 45 46 221 1397904874 46 47 55 1397905095 47 48 50 1397905150 48 49 10 1397905200 49 50 86 1397905210 50 51 225 1397905296 51 52 111 1397905521 52 58 7154 1397905632 58 57 3845 1397912786 57 71 868 1397916631 71 59 45 1397917499 59 61 100 1397917544 61 45 423 1397917644 45 46 616 1397918067 46 47 50 1397918683 47 48 35 1397918733 48 49 10 1397918768 49 40 96 1397918778 40 28 40 1397918874 28 27 35 1397918914 27 26 85 1397918949

26 2 105 1397919034 31 29 480 1399091565 29 39 420 1399092045 39 49 180 1399092465 49 47 120 1399092645 47 46 19590 1399092765 46 48 60 1399112355 48 28 240 1399112415 28 27 60 1399112655 27 31 240 1399112715 31 2 1601 1399112955 18 71 3000 1399631363 31 30 230 1399700124 30 36 2897 1399700354 36 2 1859 1399703251 2 36 5035 1399705110 36 38 200 1399710145 38 52 171 1399710345 52 29 16361 1399710516 29 2 340 1399726877 9 7 120 1402121269 7 5 220 1402121389 5 61 34143 1402121609 61 46 6103 1402155752 46 48 70 1402161855 48 49 5 1402161925 49 28 146 1402161930 28 27 35 1402162076

27 26 55 1402162111 25 15 291 1418968043 15 31 226 1418968334 31 30 251 1418968560 30 68 1289 1418968811 68 53 91 1418970100 53 51 196 1418970191 51 66 352 1418970387 66 65 196 1418970739 65 51 1763 1418970935 51 36 1106 1418972698 36 37 266 1418973804 37 29 97 1418974070 29 27 179 1418974167 27 28 35 1418974346 28 1 94 1418974381 1 28 312 1418974475 28 27 30 1418974787 27 29 138 1418974817 29 37 57 1418974955 37 36 813 1418975012 36 42 65 1418975825 42 67 115 1418975890 25 14 301 1419565537 14 34 110 1419565838 34 33 76 1419565948 33 2 82 1419566024 2 26 87 1419566106

26 27 50 1419566193 27 28 20 1419566243 28 40 56 1419566263 40 49 85 1419566319 49 48 15 1419566404 48 47 30 1419566419 47 45 134 1419566449 45 44 72 1419566583 44 43 174 1419566655 62 45 995 1419659188 45 46 32343 1419660183 46 47 75 1419692526 20 19 211 1420805110 19 18 40 1420805321 18 41 60 1420805361 56 65 257 1420864876 65 66 191 1420865133 27 28 15 1421377182 28 40 45 1421377197 40 49 105 1421377242 49 56 6186 1421377347 56 55 241 1421383533 55 50 90 1421383774 50 39 150 1421383864 39 29 135 1421384014 29 36 375 1421384149 36 42 721 1421384524 42 67 195 1421385245

67 45 16737 1421385440 45 59 221 1421402177 59 71 40 1421402398 71 60 532 1421402438 16 17 392 1425107306 17 41 221 1425107698 41 48 392 1425107919 44 45 74 1425642293 45 46 196 1425642367 46 47 112 1425642563 47 48 45 1425642675 48 49 15 1425642720 49 24 191 1425642735 47 24 233 1426865727 17 18 140 1426912992 18 19 91 1426913132 19 20 120 1426913223 20 43 1486 1426913343 43 23 5945 1426914829 23 3 95 1426920774 3 4 436 1426920869 4 1 644 1426921305 1 5 160 1426921949 5 28 25 1426922109 28 5 6 1426922134 5 28 4 1426922140 28 5 6 1426922144 5 28 4 1426922150

28 5 6 1426922154 5 28 4 1426922160 28 5 6 1426922164 5 28 4 1426922170 28 6 87 1426922174 6 28 3 1426922261 28 6 10 1426922264 6 28 0 1426922274 28 7 48 1426922274 7 28 2 1426922322 28 40 201 1426922324 40 16 60 1426922525 16 39 33 1426922585 39 16 9 1426922618 16 39 4 1426922627 39 16 5655 1426922631 16 17 160 1426928286 17 41 291 1426928446 41 48 350 1426928737 48 58 994 1426929087 58 57 291 1426930081 57 61 19300 1426930372 61 45 420 1426949672 45 46 504 1426950092 46 47 262 1426950596 47 48 52 1426950858 48 41 194 1426950910 17 18 198 1428058498

18 19 95 1428058696 19 20 64 1428058791 20 19 4211 1428058855 19 18 40 1428063066 71 57 351 1428677681 57 58 608 1428678032 58 70 706 1428678640 70 47 532 1428679346 47 48 50 1428679878 48 41 171 1428679928

b) Travel time of the links for weekend days

Origin Destination Travel time Unix time of (sec) Origin 34 49 7051 1394938434 49 52 5684 1394945485 52 49 13031 1394951169 49 45 546 1394964200 45 46 12624 1394964746 46 20 9062 1394977370 20 23 1083 1394986432 45 46 180 1395074108 46 47 60 1395074288 47 1 240 1395074348 49 48 120 1395116848 48 57 781 1395116968

57 71 1981 1395117749 71 69 8910 1395119730 69 23 240 1395128640 23 3 60 1395128880 3 4 240 1395128940 4 5 135 1395129180 5 6 60 1395129315 6 10 180 1395129375 10 12 120 1395129555 12 13 120 1395129675 13 2 5471 1395129795 2 27 180 1395135266 27 38 180 1395135446 38 68 60 1395135626 68 53 120 1395135686 53 38 6654 1395135806 38 29 246 1395142460 29 30 541 1395142706 27 39 120 1395218709 39 38 60 1395218829 38 68 120 1395218889 68 48 2067 1395219009 48 46 360 1395221076 46 45 480 1395221436 45 48 11171 1395221916 48 1 480 1395233087 1 2 480 1395233567 31 35 2251 1395292120

35 33 7738 1395294371 33 22 180 1395302109 22 8 300 1395302289 8 4 240 1395302589 4 20 20254 1395302829 20 41 1732 1395323083 41 49 223 1395324815 49 50 60 1395325038 50 51 300 1395325098 51 54 240 1395325398 54 53 5322 1395325638 53 68 60 1395330960 68 2 302 1395331020 34 33 106 1397360389 33 2 110 1397360495 2 26 517 1397360605 26 27 115 1397361122 27 28 25 1397361237 28 40 80 1397361262 40 39 30 1397361342 39 68 280 1397361372 68 53 96 1397361652 53 31 18895 1397361748 31 2 151 1397380643 2 26 100 1397380794 26 27 135 1397380894 27 28 75 1397381029 28 40 65 1397381104

40 49 953 1397381169 49 48 25 1397382122 48 57 3463 1397382147 57 71 215 1397385610 71 59 311 1397385825 59 61 100 1397386136 61 62 161 1397386236 62 44 170 1397386397 44 43 312 1397386567 43 44 16159 1397386879 44 61 260 1397403038 61 45 582 1397403298 45 46 396 1397403880 46 47 60 1397404276 47 41 461 1397404336 41 24 70 1397404797 24 28 65 1397404867 28 27 55 1397404932 27 26 96 1397404987 26 2 165 1397405083 26 27 151 1397556042 27 28 50 1397556193 28 40 50 1397556243 40 49 130 1397556293 49 48 31 1397556423 48 64 8147 1397556454 64 63 60 1397564601 63 62 176 1397564661

62 61 170 1397564837 61 62 2495 1397565007 62 63 95 1397567502 63 64 95 1397567597 64 63 2538 1397567692 63 62 55 1397570230 62 61 140 1397570285 61 45 872 1397570425 45 46 186 1397571297 46 47 130 1397571483 47 48 45 1397571613 48 49 61 1397571658 49 40 100 1397571719 40 28 60 1397571819 28 27 65 1397571879 27 26 135 1397571944 26 2 156 1397572079 31 2 125 1398247231 2 26 126 1398247356 26 27 105 1398247482 27 28 25 1398247587 28 40 55 1398247612 40 49 401 1398247667 49 48 171 1398248068 48 57 1779 1398248239 57 71 60 1398250018 71 59 25 1398250078 59 61 211 1398250103

61 62 56 1398250314 62 43 687 1398250370 43 44 9487 1398251057 44 62 105 1398260544 62 61 135 1398260649 61 45 1268 1398260784 45 46 747 1398262052 46 47 116 1398262799 47 48 390 1398262915 48 49 56 1398263305 49 40 95 1398263361 40 28 140 1398263456 28 27 95 1398263596 27 26 256 1398263691 26 2 155 1398263947 26 27 672 1398572760 27 28 255 1398573432 28 40 96 1398573687 40 39 180 1398573783 39 38 216 1398573963 38 68 60 1398574179 68 53 501 1398574239 53 28 6478 1398574740 28 27 841 1398581218 27 26 240 1398582059 26 31 7284 1398582299 31 30 396 1398589583 30 29 402 1398589979

29 68 165 1398590381 68 53 50 1398590546 53 52 2997 1398590596 52 66 181 1398593593 66 55 152 1398593774 55 56 150 1398593926 56 57 1672 1398594076 57 71 70 1398595748 71 59 41 1398595818 59 61 130 1398595859 61 62 60 1398595989 62 44 111 1398596049 44 43 250 1398596160 43 44 11153 1398596410 44 62 215 1398607563 62 61 175 1398607778 61 45 1364 1398607953 45 46 325 1398609317 46 47 416 1398609642 47 48 30 1398610058 48 49 10 1398610088 49 40 95 1398610098 40 28 51 1398610193 28 27 85 1398610244 27 26 110 1398610329 26 2 90 1398610439 2 31 90 1398610529 35 30 230 1398745796

30 29 827 1398746026 29 38 131 1398746853 38 68 85 1398746984 68 53 190 1398747069 53 38 3661 1398747259 38 29 461 1398750920 29 30 386 1398751381 30 31 221 1398751767 31 2 115 1398751988 2 34 3156 1398752103 34 33 81 1398755259 33 32 431 1398755340 32 22 90 1398755771 22 9 126 1398755861 9 7 110 1398755987 7 6 50 1398756097 6 69 6437 1398756147 69 23 181 1398762584 23 3 40 1398762765 3 21 210 1398762805 21 19 522 1398763015 19 18 102 1398763537 18 41 66 1398763639 41 24 41 1398763705 24 40 85 1398763746 40 39 30 1398763831 39 49 2330 1398763861 49 48 25 1398766191

48 47 40 1398766216 47 46 131 1398766256 46 45 130 1398766387 45 44 251 1398766517 44 43 286 1398766768 43 44 3753 1398767054 44 45 176 1398770807 45 46 170 1398770983 46 47 101 1398771153 47 48 30 1398771254 48 49 25 1398771284 49 40 200 1398771309 40 28 106 1398771509 28 27 55 1398771615 27 26 200 1398771670 26 2 126 1398771870 31 29 524 1399174728 29 51 540 1399175252 51 56 3370 1399175792 56 71 2190 1399179162 71 59 120 1399181352 59 63 8152 1399181472 63 62 60 1399189624 62 71 240 1399189684 71 60 240 1399189924 60 50 3269 1399190164 50 39 360 1399193433 39 29 421 1399193793

29 30 180 1399194214 30 2 300 1399194394 2 31 4700 1399194694 31 29 1440 1399199394 29 38 181 1399200834 38 68 180 1399201015 68 53 4869 1399201195 53 68 60 1399206064 68 29 661 1399206124 29 30 423 1399206785 30 2 181 1399207208 26 18 11430 1399265002 18 17 175 1399276432 26 18 1542 1400043375 29 50 443 1400417755 50 47 1238 1400418198 47 61 699 1400419436 61 63 330 1400420135 38 39 81 1400472762 39 48 396 1400472843 48 49 16 1400473239 49 48 107 1400473255 48 47 106 1400473362 47 46 302 1400473468 46 45 271 1400473770 17 18 180 1400647174 18 47 321 1400647354 47 46 66 1400647675

100

46 45 466 1400647741 45 44 250 1400648207 44 62 131 1400648457 62 64 115 1400648588 64 71 9587 1400648703 71 57 222 1400658290 57 58 151 1400658512 58 47 1063 1400658663 47 48 497 1400659726 48 24 165 1400660223 24 1 141 1400660388 35 2 4826 1400736709 2 4 3269 1400741535 4 9 22476 1400744804 9 34 400 1400767280 64 62 182 1400984718 62 44 598 1400984900 44 45 1448 1400985498 45 47 393 1400986946 47 48 45 1400987339 48 49 23 1400987384 49 39 401 1400987407 39 49 29798 1400987808 49 46 524 1401017606 46 44 320 1401018130 46 47 94 1401073167 47 39 546 1401073261 39 34 20261 1401073807

101

34 9 411 1401094068 9 46 8727 1401094479 46 45 274 1401103206 45 44 97 1401103480 44 64 313 1401103577 44 45 172 1401157839 45 46 718 1401158011 46 47 67 1401158729 47 48 61 1401158796 48 39 307 1401158857 64 46 1977 1401243570 46 47 108 1401245547 47 40 171 1401245655 40 39 159 1401245826 39 35 20757 1401245985 35 26 1112 1401266742 26 9 300 1401267854 9 49 8778 1401268154 49 48 284 1401276932 48 47 291 1401277216 47 62 1236 1401277507 62 64 221 1401278743 64 4 10706 1401278964 4 6 241 1401289670 6 7 40 1401289911 7 34 495 1401289951 7 4 280 1401698632 4 7 27814 1401698912

102

7 34 441 1401726726 29 47 3737 1401870665 47 45 480 1401874402 45 44 341 1401874882 44 69 18092 1401875223 69 23 1151 1401893315 23 3 461 1401894466 3 4 350 1401894927 4 5 110 1401895277 5 6 50 1401895387 6 7 70 1401895437 7 9 210 1401895507 9 10 20 1401895717 10 11 50 1401895737 11 12 41 1401895787 12 13 120 1401895828 13 14 130 1401895948 14 34 120 1401896078 7 61 20357 1402476979 61 58 1445 1402497336 45 61 231 1418884553 61 62 156 1418884784 62 63 135 1418884940 63 64 150 1418885075 64 63 4690 1418885225 63 62 106 1418889915 62 61 164 1418890021 61 45 418 1418890185

103

45 47 436 1418890603 47 48 91 1418891039 48 49 45 1418891130 49 39 276 1418891175 39 38 315 1418891451 38 29 792 1418891766 29 30 699 1418892558 30 35 725 1418893257 35 31 296 1418893982 31 15 141 1418894278 15 25 266 1418894419 3 69 210 1419490563 71 57 465 1419781125 57 58 165 1419781590 58 46 694 1419781755 46 47 90 1419782449 47 48 30 1419782539 48 41 105 1419782569 17 18 300 1419943964 18 47 371 1419944264 47 70 40 1419944635 70 46 101 1419944675 46 58 160 1419944776 58 57 251 1419944936 57 59 7192 1419945187 59 61 128 1419952379 61 45 315 1419952507 45 46 130 1419952822

104

46 47 60 1419952952 47 41 183 1419953012 59 57 265 1420038379 57 58 251 1420038644 58 46 333 1420038895 46 41 484 1420039228 41 16 602 1420039712 43 44 233 1420081793 44 45 148 1420082026 45 46 144 1420082174 46 47 63 1420082318 47 27 277 1420082381 27 26 80 1420082658 26 2 70 1420082738 2 31 90 1420082808 31 17 33984 1420082898 17 18 190 1420116882 18 47 314 1420117072 47 46 832 1420117386 46 45 269 1420118218 45 59 325 1420118487 59 71 24 1420118812 71 57 11204 1420118836 57 58 199 1420130040 58 70 162 1420130239 70 47 36 1420130401 47 41 168 1420130437 67 42 228 1420950270

105

42 36 150 1420950498 36 37 331 1420950648 37 29 186 1420950979 29 28 451 1420951165 28 1 116 1420951616 1 16 1029 1420951732 16 17 527 1420952761 17 16 16 1420953288 16 17 4 1420953304 17 16 1 1420953308 16 17 4 1420953309 17 16 1 1420953313 16 17 4 1420953314 17 18 126 1420953318 18 17 175 1420953444 17 18 171 1420953619 18 46 91 1420953790 46 18 5 1420953881 18 46 5 1420953886 46 18 5 1420953891 18 46 0 1420953896 46 47 217 1420953896 47 45 224 1420954113 45 61 1719 1420954337 61 62 86 1420956056 62 63 115 1420956142 63 64 125 1420956257 64 61 2512 1420956382

106

61 45 158 1420958894 45 62 108 1420959052 62 46 83 1420959160 46 62 2 1420959243 62 46 3 1420959245 46 62 2 1420959248 62 47 38 1420959250 47 48 50 1420959288 48 49 10 1420959338 49 39 259 1420959348 39 38 80 1420959607 38 29 298 1420959687 29 37 51 1420959985 37 36 1367 1420960036 36 42 336 1420961403 42 67 261 1420961739 42 36 141 1421034075 36 37 160 1421034216 37 29 176 1421034376 29 27 451 1421034552 27 28 40 1421035003 28 1 7236 1421035043 1 28 222 1421042279 28 27 30 1421042501 27 29 346 1421042531 29 37 327 1421042877 37 36 916 1421043204 36 42 390 1421044120

107

42 67 240 1421044510 67 59 5420 1421044750 59 61 215 1421050170 61 44 484 1421050385 36 37 315 1421130723 37 29 316 1421131038 29 27 285 1421131354 27 28 60 1421131639 28 27 15291 1421131699 27 29 510 1421146990 29 37 270 1421147500 37 36 646 1421147770 1 28 3311 1421649049 16 24 106 1421842886 24 49 182 1421842992 49 48 30 1421843174 48 47 42 1421843204 47 46 121 1421843246 4 69 10513 1425385289 69 23 782 1425395802 23 3 573 1425396584 3 4 341 1425397157 4 5 231 1425397498 5 8 431 1425397729 8 16 352 1425398160 31 2 90 1425472438 2 1 403 1425472528 1 16 525 1425472931

108

58 57 244 1425544475 57 71 222 1425544719 71 58 178 1425544941 58 60 134 1425545119 60 58 159 1425545253 46 45 164 1425907920 45 44 173 1425908084 44 45 8952 1425908257 45 46 581 1425917209 46 47 242 1425917790 47 41 174 1425918032 47 46 231 1426074445 58 46 190 1426600535 48 49 13 1427115184 49 48 50 1427115197 48 47 366 1427115247 47 46 151 1427115613 46 45 413 1427115764 45 44 329 1427116177 44 63 166 1427116506 63 71 9716 1427116672 71 57 206 1427126388 57 58 246 1427126594 58 46 778 1427126840 46 48 176 1427127618 48 24 136 1427127794 41 48 200 1427295827 48 49 180 1427296027

109

49 47 268 1427296207 47 46 174 1427296475 46 58 108 1427296649 58 57 616 1427296757 17 46 1720 1427629368 46 45 319 1427631088 45 61 150 1427631407 61 59 214 1427631557 8 5 197 1427692412 5 4 78 1427692609 4 3 655 1427692687 3 69 249 1427693342 43 44 211 1427960350 44 45 84 1427960561 45 46 786 1427960645 46 47 93 1427961431 47 41 229 1427961524 41 46 25191 1427961753 46 47 140 1427986944 47 48 60 1427987084 48 41 140 1427987144

110

Appendix C Section C.1.2.: Travel time of the links in different time for weekdays and weekend days.

a) Travel time of the links in different time for weekend days.

Origin Destination Distance Travel Travel Travel Travel (ft) time at time at time at time at Slot Slot Slot Slot 1(sec) 2(sec) 3(sec) 4(sec) 1 28 1783.78 0 312 0 0 2 26 1842.63 87 0 121 0 3 4 3363.11 0 436 0 0 5 4 1846.01 0 130 0 0 6 5 1045.42 0 51 0 0 7 6 1035.75 0 56 0 0 9 7 4022.17 0 117.5 0 0 10 9 316.33 0 10 0 0 14 34 2464.84 110 0 0 0 15 31 1780.58 226 0 0 0 16 17 1828.56 0 276 0 0 17 18 1466.66 140 0 198 0 17 41 1940.88 0 256 0 0 18 19 642.67 91 0 95 0 18 41 1327.53 0 0 60 0 19 18 642.67 0 0 40 0 19 20 724.2 120 0 64 0 20 19 724.2 0 0 211 0 20 43 9107.48 1486 0 0 0 22 10 2881.58 0 175 0 0 23 3 1298.46 0 95 0 0 25 14 1031.98 301 0 0 0

111

25 15 2446.75 291 0 0 0 26 2 1842.63 0 0 0 90 26 27 2393.58 115 0 124 0 27 26 2393.58 0 0 0 80.5 27 28 599.96 17.5 35 56 0 27 29 2595.08 0 138 0 0 28 1 1783.78 0 94 0 0 28 27 599.96 0 30 60 29.5 28 40 1320.98 50.5 201 245 0 29 27 2595.08 0 179 0 0 29 30 1686.4 0 0 0 180 29 37 887.14 0 57 0 0 31 2 1895.66 0 80 124 0 31 30 2632.87 240.5 0 0 0 32 22 1670.2 0 65 0 0 33 2 1468.29 82 0 0 0 34 33 1163.35 76 0 0 0 36 37 1435.23 0 266 0 0 36 42 647.53 0 65 0 0 37 29 887.14 0 97 0 0 38 39 1400.21 0 60 0 0 40 28 1320.98 0 0 0 40 40 49 3455.93 95 0 236 0 41 48 2870.22 0 371 0 0 42 67 1644.48 195 115 0 0 43 44 6759 0 0 511 0 44 43 6759 174 0 0 0 44 45 2052.01 0 0 82 0

112

45 44 2052.01 72 0 0 0 45 46 3068.21 0 0 208.5 543.6667 45 61 3107.61 0 0 822 0 46 45 3068.21 0 0 638 0 46 47 1470.55 0 0 83.5 169.5 47 46 1470.55 0 150 242 0 47 48 1203.49 0 0 47.5 46.75 48 41 2870.22 0 0 0 182.5 48 47 1203.49 30 420 140 0 48 49 464.16 0 0 12.5 10 49 40 3455.93 0 0 0 103.5 49 48 464.16 15 60 110 0 49 50 1210.76 0 0 103.5 0 50 39 3401.85 150 0 0 0 50 49 1210.76 0 0 60 0 50 51 1996.06 0 0 225 0 51 50 1996.06 0 0 261 0 51 52 1394.96 0 0 111 0 51 66 2898.11 0 352 0 0 53 51 2332.35 0 196 0 0 55 50 1622.45 90 0 0 0 56 55 2480.72 241 0 0 720 56 65 2535.47 257 0 0 0 57 58 3420.24 0 0 0 608 58 57 3420.24 0 291 0 0 58 70 3088.44 0 0 0 706 59 61 3237.04 0 0 0 141.5 59 71 792.7 0 40 30 0

113

61 45 3107.61 0 0 0 421.5 61 59 3237.04 0 0 115.5 0 65 66 612.77 191 0 0 0 66 65 612.77 0 196 0 0 68 53 1264.2 0 91 0 0 71 57 2079.97 0 0 0 351

b) Travel time of the links in different time for weekdays.

Origin Destination Distance Travel Travel Travel Travel (ft) time at time at time at time at Slot Slot 2 Slot 3 Slot 4 1(sec) (sec) (sec) (sec) 1 28 1783.78 222 0 0 0 2 26 1842.63 517 100 126 0 2 31 1895.66 90 0 0 90 3 4 3363.11 0 240 0 345.5 3 21 6133.28 0 210 0 0 4 3 3363.11 655 0 0 0 4 5 1846.01 0 135 0 170.5 5 4 1846.01 78 0 0 0 5 6 1045.42 0 60 0 50 5 8 1942.55 0 0 0 431 6 7 1035.75 0 0 0 55 7 6 1035.75 0 50 0 0 7 9 4022.17 0 0 0 210 8 5 1942.55 197 0 0 0 8 16 1538.33 0 0 0 352 9 7 4022.17 0 110 0 0

114

9 10 316.33 0 0 0 20 10 11 1783.59 0 0 0 50 11 12 1381.91 0 0 0 41 12 13 2460.81 0 120 0 120 13 14 1670.17 0 0 0 130 14 34 2464.84 0 0 0 120 15 25 2446.75 0 266 0 0 16 17 1828.56 134.75 0 0 0 17 16 1828.56 6 0 0 0 17 18 1466.66 159 0 245 0 18 17 1466.66 175 175 0 0 18 41 1327.53 0 66 0 0 18 47 3176.68 321 0 342.5 0 19 18 642.67 0 102 0 0 23 3 1298.46 0 50 0 461 24 1 2066.59 0 141 0 0 24 40 1712.98 0 85 0 0 26 2 1842.63 70 0 126 141.5 26 27 2393.58 393.5 135 128 0 27 26 2393.58 80 240 200 149.25 27 28 599.96 32.5 67.5 37.5 0 27 29 2595.08 0 346 510 0 28 1 1783.78 116 0 0 0 28 27 599.96 0 30 55 75 28 40 1320.98 88 65 52.5 0 29 27 2595.08 451 285 0 0 29 30 1686.4 0 283 482 0 29 37 887.14 0 51 270 0

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29 38 1642.62 131 0 181 0 30 29 1686.4 0 402 0 0 30 31 2632.87 0 221 0 0 31 2 1895.66 0 133 107.5 0 31 15 1780.58 0 141 0 0 31 30 2632.87 0 396 0 0 32 22 1670.2 0 90 0 0 33 2 1468.29 110 0 0 0 33 32 1643.51 0 431 0 0 34 33 1163.35 106 81 0 0 35 30 1004.91 230 0 0 0 35 31 2287.33 0 296 0 0 36 37 1435.23 245.5 315 0 0 37 29 887.14 181 316 0 0 38 29 1642.62 461 298 246 0 38 39 1400.21 81 0 0 0 38 68 795.03 72.5 90 180 0 39 38 1400.21 216 151.6667 0 0 40 28 1320.98 0 0 106 83.66667 40 39 1476.28 123 30 0 0 40 49 3455.93 0 953 265.5 0 41 24 1388.03 0 41 0 70 41 48 2870.22 0 0 0 200 42 36 647.53 145.5 0 0 0 42 67 1644.48 0 250.5 0 0 43 44 6759 233 211 0 0 44 43 6759 0 0 282.6667 0 44 45 2052.01 160 84 176 0

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44 62 1862.87 131 0 105 215 45 44 2052.01 250 341 212.5 0 45 46 3068.21 431 786 170 363.5714 45 61 3107.61 0 231 150 0 46 45 3068.21 368.5 480 261.5 0 46 47 1470.55 109.8 93 101 146 46 58 2053.28 0 0 160 108 47 46 1470.55 184 0 158.5 174 47 48 1203.49 53 70.5 30 111 47 70 692 0 0 40 0 48 41 2870.22 0 0 0 122.5 48 47 1203.49 106 0 184.75 0 48 49 464.16 19.5 27.5 19 42.33333 49 40 3455.93 0 0 200 96.66667 49 48 464.16 113.5 25 34 0 49 50 1210.76 0 0 0 60 50 39 3401.85 0 360 0 0 50 51 1996.06 0 0 0 300 53 68 1264.2 0 0 60 60 55 56 2480.72 0 0 150 0 56 57 4572.91 0 0 1672 0 57 58 3420.24 0 151 0 215.25 57 71 2079.97 0 222 115 0 58 46 2053.28 0 0 190 513.5 58 57 3420.24 0 244 251 616 58 70 3088.44 0 0 0 162 59 61 3237.04 0 215 147 128 59 71 792.7 0 0 24 0

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61 45 3107.61 0 288 0 589.6667 61 59 3237.04 0 0 214 0 61 62 2382.91 0 121 92.33333 0 62 44 1862.87 598 0 140.5 0 62 61 2382.91 0 164 148.3333 175 62 63 2060.03 0 125 95 0 63 62 2060.03 0 83 115.5 0 63 64 2294.83 0 137.5 95 0 64 63 2294.83 0 0 60 0 66 55 2600.28 0 0 152 0 67 42 1644.48 228 0 0 0 68 53 1264.2 143 85 0 0 69 23 3951.93 0 210.5 0 966.5 70 46 1467.32 0 0 101 0 70 47 692 0 0 0 36 71 57 2079.97 0 222 0 335.5

Appendix C Section C.1.3.: TravellingSpeed of different links for different times of weekdays and weekends. a) TravellingSpeed of different links for different times of weekend days.

Origin Destination Distance Travel Travel Travel Travel (ft) speed at speed at speed at speed at Slot 1 Slot 2 Slot 3 Slot 4 (km/hr) (km/hr) (km/hr) (km/hr) 36 37 1435.23 0 5.920486 0 0 36 42 647.53 0 10.9311 0 0 38 39 1400.21 0 25.60704 0 0

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40 49 3455.93 39.91708 0 16.06832 36.63887 41 48 2870.22 0 8.489043 0 17.25718 42 67 1644.48 9.253615 15.69091 0 0 43 44 6759 42.62365 0 14.51373 0 44 45 2052.01 31.27263 0 27.4589 0 45 46 3068.21 0 0 10.71205 6.192555 45 61 3107.61 0 0 4.148319 8.08996 46 47 1470.55 0 10.75737 12.9962 9.519794 47 48 1203.49 44.01885 3.144204 18.61699 28.24739 48 49 464.16 33.95423 8.488558 22.6876 50.93135 49 50 1210.76 0 0 17.48927 0 50 39 3401.85 24.88521 0 0 0 50 51 1996.06 0 0 9.063048 0 51 52 1394.96 0 0 13.78975 0 51 66 2898.11 0 9.034199 0 0 53 51 2332.35 0 13.05735 0 0 55 50 1622.45 19.78091 0 0 0 56 55 2480.72 11.29479 0 0 3.780617 56 65 2535.47 10.82537 0 0 0 57 58 3420.24 0 12.89677 0 6.172633 58 70 3088.44 0 0 0 4.800118 59 61 3237.04 0 0 30.75272 25.10204 59 71 792.7 0 21.74535 28.9938 0 65 66 612.77 3.520316 3.430512 0 0 68 53 1264.2 0 15.24375 0 0 71 57 2079.97 0 0 0 6.502306 45 47 4892.29 3.70628 0 0 0 18 47 3176.68 3.379447 0 0 0 15 67 1911.39 8.688136 0 0 0 39 40 1476.28 8.84 0 0 0 2 31 1895.66 5.494667 0 0 0

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49 50 1210.76 10.08967 0 0 0 57 71 2079.97 0 3.661919 0 0 46 58 2053.28 0 3.570922 0 0 16 22 3406.7 0 2.712341 0 0 37 68 2206.85 0 2.581111 0 0 40 49 3455.93 0 2.373578 0 0 16 5 3526.3 0 3.547586 0 0 1 22 3695.11 0 1.9909 0 0 1 16 2176.9 0 1.517003 0 0 2 27 4261.86 0 2.145952 0 0 46 20 2821.39 0 0 3.254198 0 18 47 3176.68 0 0 3.336849 0 43 20 9107.48 0 0 2.221337 0 41 40 3018.84 0 0 2.399714 0 71 59 792.7 0 0 0 3.096484 47 70 692 0 0 0 2.43662 50 55 1622.45 0 0 0 5.070156 49 50 1210.76 0 0 0 7.473827 27 29 2595.08 0 0 0 3.949893 40 39 1200 0 10 0 0 8 1 3631 4.034444 0 0 0 17 1 4043 3.369167 0 0 0 40 49 3455 5.315385 0 0 0 29 30 1640 4.1 0 0 0 30 31 2460 6.15 0 0 0 29 37 900 0 2.25 0 0 59 71 900 0 2.25 0 0 36 42 900 0 2.25 0 0 7 17 4552 0 4.635438 0 0 1 17 4119 0 3.4325 0 0 22 9 3192 0 3.711628 0 0

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1 22 3658 0 6.650909 0 0 31 34 1655 0 6.895833 0 0 9 12 3337 0 3.770621 0 0 41 8 5210 0 0 0 3.427632 48 41 2870.22 24.3239 0 0 0 41 24 1388.03 14.16357 0 0 0 18 41 1327.53 15.25897 0 0 0 24 40 1712.98 15.29446 0 0 0 38 29 1642.62 15.94777 0 0 0 38 39 1400.21 21.2153 0 0 0 27 29 2595.08 29.82851 0 0 0 37 29 887.14 21.12238 0 0 0 38 29 1642.62 25.27108 0 0 0 38 68 795.03 36.13773 0 0 0 26 30 2008.48 12.87487 0 0 0 29 30 1686.4 16.37282 0 0 0 38 29 1642.62 0 18.25133 0 0 38 68 795.03 0 14.45509 0 0 41 24 1388.03 0 15.59584 0 0 18 41 1327.53 0 12.17917 0 0 24 40 1712.98 0 16.79392 0 0 18 47 3176.68 0 13.57556 0 0 50 39 3401.85 0 19.11152 0 0 49 50 1210.76 0 8.350069 0 0 50 51 1996.06 0 16.22813 0 0 48 41 2870.22 0 0 23.9185 0 50 39 3401.85 0 0 25.57782 0 50 39 3401.85 0 0 23.78916 0 39 40 1476.28 0 0 16.96874 0 38 39 1400.21 0 0 14.28786 0 38 29 1642.62 0 0 13.6885 0

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38 68 795.03 0 0 9.138276 0 29 30 1686.4 0 0 14.05333 0 30 31 2632.87 0 0 23.29973 0 37 29 887.14 0 0 13.04618 0 52 53 1689.16 0 0 14.43726 0 53 68 1264.2 0 0 12.9 0 50 51 1996.06 0 0 0 16.09726 51 53 2332.35 0 0 0 21.20318 53 68 1264.2 0 0 0 28.09333 38 68 795.03 0 0 0 12.23123 38 29 1642.62 0 0 0 21.05923 37 29 887.14 0 0 0 21.63756 38 39 1400.21 0 0 0 17.95141 50 39 3401.85 0 0 0 17.90447 24 40 1712.98 0 0 0 19.24697 41 24 1388.03 0 0 0 15.42256

b) TravellingSpeed of different links for different times of week days. Origin Destination Distance Travel Travel Travel Travel (ft) speed at speed at speed at speed at Slot 1 Slot 2 Slot 3 Slot 4 (km/hr) (km/hr) (km/hr) (km/hr) 1 28 1783.78 12.84501 0 0 0 2 26 1842.63 16.39741 20.21881 16.04667 14.28891 2 31 1895.66 23.11189 15.63962 19.34949 23.11189 3 4 3363.11 5.634005 15.37614 0 10.68096 3 21 6133.28 0 32.04726 0 0 4 5 1846.01 25.9691 15.00437 0 11.88029 5 6 1045.42 0 19.11864 0 22.94237 5 8 1942.55 10.8199 0 0 4.945525 6 7 1035.75 0 22.73016 0 20.66378

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7 9 4022.17 0 40.12224 0 21.01641 8 16 1538.33 0 0 0 4.795394 9 10 316.33 0 0 0 17.35513 10 11 1783.59 0 0 0 39.14195 11 12 1381.91 0 0 0 36.98396 12 13 2460.81 0 22.50165 0 22.50165 13 14 1670.17 0 0 0 14.09726 14 34 2464.84 0 0 0 22.5385 15 25 2446.75 0 10.09312 0 0 16 17 1828.56 174.6486 0 0 0 17 18 1466.66 9.658912 9.19621 6.568721 0 18 41 1327.53 0 22.07079 0 0 18 47 3176.68 10.8589 0 10.17725 0 19 18 642.67 0 6.913617 0 0 23 3 1298.46 0 28.49548 0 3.090616 24 1 2066.59 0 16.08247 0 0 24 40 1712.98 0 22.11316 0 0 26 27 2393.58 19.75244 15.19923 16.82555 17.5975 27 28 599.96 20.25613 15.84854 14.76242 8.777655 27 29 2595.08 6.313812 9.110592 5.583391 0 28 40 1320.98 16.47142 22.29977 20.64181 17.32452 29 30 1686.4 0 5.57091 3.839114 0 29 37 887.14 5.378127 11.08379 3.605337 0 29 38 1642.62 8.834339 6.048369 8.642488 0 30 31 2632.87 0 10.18391 0 0 31 15 1780.58 0 13.8567 0 0 32 22 1670.2 0 20.36308 0 0 33 2 1468.29 14.64659 0 0 0

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33 32 1643.51 0 4.184201 0 0 34 33 1163.35 12.04265 15.75951 0 0 35 30 1004.91 4.794207 0 0 0 35 31 2287.33 0 8.479194 0 0 36 37 1435.23 6.414864 4.999521 0 0 38 39 1400.21 13.04062 10.13026 0 0 38 68 795.03 12.0327 9.693006 4.846503 0 40 39 1476.28 13.16986 53.99642 0 0 40 49 3455.93 0 3.979143 16.62178 39.22886 41 24 1388.03 0 37.14775 0 21.75797 41 48 2870.22 0 0 0 20.72842 42 36 647.53 4.883311 0 0 0 42 67 1644.48 7.914276 7.203413 0 0 43 44 6759 31.83054 35.14936 26.23767 0 44 45 2052.01 11.5396 16.70407 11.69463 0 44 62 1862.87 9.510976 0 17.0081 9.507395 45 46 3068.21 8.473762 5.648621 16.33927 9.260039 45 61 3107.61 0 13.30077 22.73279 5.78279 46 47 1470.55 11.73273 17.35059 13.07838 10.16284 46 58 2053.28 0 0 12.96971 12.62445 47 48 1203.49 18.68725 18.73143 25.58335 11.89699 47 70 692 0 0 18.98294 21.09216 48 49 464.16 15.30299 19.44651 20.89289 12.03103 49 50 1210.76 0 0 0 22.14238 50 39 3401.85 0 10.36884 0 0 50 51 1996.06 0 0 0 7.300789 53 68 1264.2 9.700569 16.31978 23.11969 23.11969 55 56 2480.72 0 0 18.14696 0

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56 57 4572.91 0 0 3.001054 0 57 58 3420.24 0 20.11752 14.95204 11.76391 57 71 2079.97 0 10.28067 19.84617 6.802711 58 70 3088.44 0 0 0 20.91903 59 61 3237.04 0 16.52065 20.38035 27.74953 59 71 792.7 0 0 36.24224 0 61 62 2382.91 0 18.77633 22.97279 14.94125 62 63 2060.03 0 22.65877 21.68241 0 63 64 2294.83 0 18.31324 34.23693 0 66 55 2600.28 0 0 18.77128 0 69 23 3951.93 0 20.60035 0 4.486677 70 46 1467.32 0 0 15.9412 0 8 1 3631 4.034444 0 0 0 17 1 4043 3.369167 0 0 0 40 49 3455 5.315385 0 0 0 29 30 1640 4.1 0 0 0 30 31 2460 6.15 0 0 0 29 37 900 0 2.25 0 0 59 71 900 0 2.25 0 0 36 42 900 0 2.25 0 0 7 17 4552 0 4.635438 0 0 1 17 4119 0 3.4325 0 0 22 9 3192 0 3.711628 0 0 1 22 3658 0 6.650909 0 0 31 34 1655 0 6.895833 0 0 9 12 3337 0 3.770621 0 0 31 34 1604 0 0 0 8.911111 41 8 5210 0 0 0 3.427632

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51 53 2332 0 0 0 15.54667 18 41 1327 9.478571 0 0 0 41 24 1388 11.104 0 0 0 24 40 1712 10.7 0 0 0 37 68 2206 10.02727 0 0 0 51 53 2332 8.969231 0 0 0 50 51 1996 16.22764 0 0 0 49 50 1210 12.87234 0 0 0 50 39 3401 9.857971 0 0 0 50 51 1996 0 23.48235 0 0 49 50 1210 0 13.44444 0 0 37 68 2206 0 15.75714 0 0 18 47 3176 0 15.05213 0 0 48 41 2870 0 13.66667 0 0 24 40 1712 0 16.30476 0 0 18 41 1327 0 0 15.25287 0 48 41 2870 0 0 14.87047 0 41 24 1388 0 0 18.26316 0 24 40 1712 0 0 12.68148 0 50 39 3401 0 0 10.97097 0 49 50 1210 0 0 10.80357 0 50 55 1622 0 0 15.90196 0 38 39 1400 0 0 14.43299 0 39 40 1476 0 0 13.17857 0 41 24 1388.03 0 0 0 6.516573 24 40 1712.98 0 0 0 8.69533 37 68 2206.85 0 0 0 23.72957 37 29 887.14 0 0 0 21.12238

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36 37 1435.23 0 0 0 12.48026 38 68 795.03 0 0 0 34.56652 38 39 1400.21 0 0 0 16.09437 39 40 1476.28 0 0 0 18.92667 1 28 1783.78 0 0 0 15.51113 16 17 1828.56 0 0 0 8.584789 17 1 4043 3.369167 0 0 0 18 41 1327.53 0 0 0 24.58389 48 41 2870.22 0 0 0 29.28796 18 19 642.67 0 0 0 14.94581 18 47 3176.68 0 0 0 26.92102

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APPENDIX D: INTERSECTION DELAY DATA This appendix contains documentation for the average intersection delay data in seconds for various nodes in weekday and weekend.

Appendix D Section D.1.1.: Data set of average intersection delay for various nodes in week days and weekend days.

Weekend day Week day

Node Intersection Name Intersection Node Intersection Name Intersection number number Delay (sec) Delay (sec) 44 Bijoy Sarani 18.9 Kamlapur Nursary 34 Mor 22.5

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56 Science Lab 67 71 Rapa Plaza Mor 78 26 Fakirapur Mor 27.5 Moddho Badda 69 link Road Mor 0

29 Dainik Bangla 51.4 Rampura TV Mor 3 center Junction 0 65 Newmartker Mor 453 Mirbag and DIT 4 Road Junction 0 30 Paltan Mor 674.25 Chowdhury Para and DIT Road 5 Junction 0 38 High court Mor 0 Chowdhury Para 6 R/A Mor 0

39 Motso Bhaban 4.2 Khilgaon Rail Mor 10 Crossing Mor 0 48 Shahbag Junction 575.4 Buddha Mandir 12 Bus stand 0 47 Bangla Motor 26.7 Mugda Bazar 13 Junction 0 46 Kawran Bazar 374.9 Mor 2 Arambag Mor 176.4 59 Mirpur Road and 21 Manik Mia avenue Junction 27 Bijoy Nagar Mor 20 57 Russsel Square 881.1 38 High court Mor 20 55 Kataban Mor 66 68 College Road Mor 99.3 68 College Road 0 Mor 53 Doel Chattar 1326.8 27 Bijoy Nagar Mor 9.7 29 62.5 31 Sapla Chattar 33 30 Paltan Mor 43 40 Saheed Captain 5 Monsur Ali Sarani 39 Motso Bhaban Mor 26.7

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45 Farmgate 8.5 31 Sapla Chattar 61.5 60 Dhanmondi 27 23.7 (Meena Bazar) Mor 35 Rajuk Mor 0 32 Motijheel Ideal 15

Collage Kamlapur Rail Intersection 33 Station 20 22 Shahjahanpur 40 Mor 22 Shahjahanpur Mor 60 10 Khilgaon Rail 5 Crossing Mor 8 Malibag Rail Gate 0 9 Shahid Baki Road 35 & Khilgaon Road Junction 41 Ramna Thana Mor 12.5 7 Malibag Bazar 7 Junction 50 Paribag Junction 120 6 Chowdhury Para 5 R/A Mor 51 TSC Mor 0 52 Bangla Academi 10 Mor 54 Palashi Mor 60 58 Panthapath Signal 81.6 Mor 26 Fakirapur Mor 94.2 18 Moghbazar Mor 89.4 28 Kakrail Mor 111. 8 36 Gulishtan Mor 161.2 Saheed Captain 40 Monsur Ali Sarani 22.3 25 Manik Nagar Mor 17.5 Mirpur Road and Manik Mia avenue 59 Junction 52

15 R.K. Mission 81 Khamar Bari Gol Road 61 Chattar 538.4 53 Doel Chattar 35 Shere Bangla 62 Nagar Mor 42.3

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66 Nilkhet Mor 55 44 Bijoy Sarani 11.25

37 Shaeed Nur 37.5 Mohakhali Hossain Square 43 Intersection 8015.3 1 Santinagar Mor 94.3 Baily Road 24 Junction 5 42 Gulistan Golap 37.5 Shah Mazar Mor 64 Agargaon Mor 1249 67 Fazle Rabbi Mor 10 63 BICC Mor 11.7 14 TT para Mor 5 66 Nilkhet Mor 25 34 Kamlapur 20 Nursary Mor 55 Kataban Mor 25 33 Kamlapur Rail 21 Station 56 Science Lab 10 62 Shere Bangla 35.25 Nagar Mor 20 Junction of 1382 Shaheed Tajuddin Ahmed Avenue & Panthapath Tejgaon Link Road 19 Dilu Road post 10 office Mor 41 Ramna Thana 20.1 Mor 16 Mouchak Mor 83.25 17 Wireless Mor 96.2 24 Baily Road 7.8 Junction 23 Hatir Jheel Link 24.5 3 Rampura TV 160

131

center Junction 70 Sonargaon Raod 251 Mor 69 Moddo Badda 10 Link Road Mor 63 BICC Mor 10 64 Agargaon Mor 0

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APPENDIX E: DETAIL CODE-MATLAB PROGRAMMING SOFTWARE This appendix contains documentation for detail code used for analysis by MATLAB programming Software.

clc ; clear; format longG

% READ DATA dt = csvread('data.csv'); %dt = [dt; csvread('data_missing.csv')]; % DELETE UNNECESSARY DATA dt(:,end) = []; dt(:,end) = []; dt(:,end) = [];

% SORT BY ASCENDING ORDER OF TIME sorted = sortrows(dt, 1); lat = sorted(:,2); lon = sorted(:,3);

%time = datestr((sorted(:,1) + 21600 )/86400 + datenum(1970,1,1), 'yyyy- mm-dd ddd HH:MM:SS');

% GET UNIX TIME unix = sorted(:,1);

% APPLY TIME CORRECTION FOR BANGLADESH newUnix = (unix + 6 * 3600) / 86400 + datenum(1970,1,1);

% GET YEAR, MONTH, DATE, DAY, HOUR, MINUTE AND SECOND

%yr = str2num(datestr(newUnix, 'yyyy')); %mnth = str2num(datestr(newUnix, 'mm')); date = str2num(datestr(newUnix, 'dd')); day = weekday(datestr(newUnix, 'yyyy-mm-dd')); hr = str2num(datestr(newUnix, 'HH')); %minute = str2num(datestr(newUnix, 'MM')); %sec = str2num(datestr(newUnix, 'SS'));

% PREPARE FORMATTED DATA TABLE z = zeros(size(unix)); dt = [unix z z date day hr z z lat lon]; save dt

% DELETE DATA BETWEEN 12AM - 8AM i = find(dt(:,6)< 8 ); dt(i,:) = [];

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%======

% FOR WEEKDAYS i = find(dt(:,5) > 5 ); dt(i,:) = [];

% FOR WEEKENDS %i = find(dt(:,5) < 6 ); dt(i,:) = [];

%======

% SPECIFY STUDY ZONE minLat = 23.72; maxLat = 23.785; minLon = 90.37; maxLon = 90.43;

% DELETE DATA OUT OF STUDY ZONE i = find(dt(:,9)< minLat | dt(:,9) > maxLat ); dt(i,:) = []; i = find(dt(:,10)< minLon | dt(:,10) > maxLon ); dt(i,:) = [];

% GET NODES COORDS nodes = csvread('nodes.txt'); nodes = sortrows(nodes, 1); edges = csvread('edges.txt'); tol = 0.000400; ndDt = []; for i=1:1:length(nodes) % FIND DATA OF NODES ONLY j = find( ( abs( dt(:,9) - nodes(i,2)) < tol) & (abs( dt(:,10) - nodes(i,3)) < tol) );

n = ones(length(j),1) * nodes(i,1); if( n > 0) thisNdDt = [dt(j,:) n]; ndDt = [ndDt; thisNdDt]; end end

% SORT BY UNIX TIME ndDt = sortrows(ndDt, 1);

%matlab len=2211; same=zeros(len,3); i=1; k=1; while(i<=len-1) value=ndDt(i,11); %disp(i);

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for j=i:len if((ndDt(j+1,11)~=value)) if((ndDt(j,4)==ndDt(i,4)) && (ndDt(j,5)==ndDt(i,5))) same(k,1)=ndDt(i,11); same(k,2)=abs(ndDt(j,1)-ndDt(i,1)); k=k+1; i=j+1; break; end else if((ndDt(j,4)~=ndDt(i,4)) || (ndDt(j,5)~=ndDt(i,5))) i=j; break; end end end if(j+1==len) same(k,1)=ndDt(i,11); same(k,2)=abs(ndDt(j+1,1)-ndDt(i,1)); end end k=1; for i=1:length(same) if(same(i,3)==1) continue; else sum=same(i,2); count=1; for j=i+1:length(same) if(same(j,1)==same(i,1)) count=count+1; sum=sum+same(j,2); same(j,3)=1; end end avrg(k,1)=same(i,1); avrg(k,2)=sum/count; k=k+1; end end

% GET ONLY 'IN TIME' OF NODE ndDtIn = ndDt(1,:); thisNode = ndDt(1,end); for i=2:1:length(ndDt) if( ndDt(i,end) ~= thisNode) ndDtIn = [ndDtIn; ndDt(i,:)]; thisNode = ndDt(i,end); end end

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% GET LINK O-D links = []; for i=1: 1: (length(ndDtIn)-1) % -1 for index of the element just before last element

% DON'T CREATE LINK IF NEW DAY if( ndDtIn(i,3) == ndDtIn(i+1,3) && ndDtIn(i,4) == ndDtIn(i+1,4) && ndDtIn(i,5) == ndDtIn(i+1,5)) O = ndDtIn(i,11); D = ndDtIn(i+1,11); T = ndDtIn(i+1) - ndDtIn(i); U = ndDtIn(i,1); links = [links; [O D T U]]; else continue; end end

% DELETE UNNECESSARY LINKS validLink = []; for i=1:1:length(links) O = links(i,1); % origin D = links(i,2); % dest T = links(i,3); % time U = links(i,4); % unix

for j=1:1:length(edges) L = edges(j,3); % length of link V = L / T * 1.09728 ; if( V >= 3.0) if( (O == edges(j,1) && D == edges(j,2)) || (D == edges(j,1) && O == edges(j,2))) validLink = [validLink; [O D L U T V]]; end end end end validLink = sortrows(validLink,[1 2]);

% GET HOUR FROM UNIX tabA = [validLink(:,[1 2 3 4 5]) unix2hr(validLink(:,4)) ]; tabA = sortrows(tabA, [1 2 6]);

% MODIFY TABLE; CONVERT TIME TO TIME RANGE for i=1:1:length(tabA)

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tabA(i,7) = hr2range(tabA(i,6)); end tabA = sortrows(tabA, [1 2 7]);

% AVERAGE DATA FOR SAME TIME RANGE tabB = [];

O = tabA(1,1); D = tabA(1,2); L = tabA(1,3); U = tabA(1,4); T = tabA(1,5); H = tabA(1,6); S = tabA(1,7); n = 1; for i=2:1:length(tabA) if ( tabA(i,1) == O && tabA(i,2) == D && tabA(i,7) == S ) T = T + tabA(i,5); n = n + 1; else avg = T / n; tabB = [tabB; [ O D L U avg H S]];

O = tabA(i,1); D = tabA(i,2); L = tabA(i,3); U = tabA(i,4); T = tabA(i,5); H = tabA(i,6); S = tabA(i,7); n = 1; end end

% DISTRIBUTE THE VALIDLINK ACCORDING TO TIME HORIZONTALLY tabC = tabB; tabC(:, [8 9 10 11 12]) = 0; for i=1:1:length(tabC) j = hr2range( tabC(i,6) ) + 7 ; % 8th col for Slot 1 tabC(i, j) = tabC(i,5); end tabC(:,7) = []; tabC = sortrows(tabC, [1 2]);

% MERGE DIFFERENT TIME SLOT TO SINGLE ROW tabD = [];

O = tabC(1,1); D = tabC(1,2); L = tabC(1,3); U = tabC(1,4); avg = tabC(1,5); H = tabC(1,6);

S1 = tabC(1,7); S2 = tabC(1,8); S3 = tabC(1,9); S4 = tabC(1,10); S5 = tabC(1,11); for i=2:1:length(tabC) if( tabC(i,1) == O && tabC(i,2) == D ) S1 = S1 + tabC(i,7);

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S2 = S2 + tabC(i,8); S3 = S3 + tabC(i,9); S4 = S4 + tabC(i,10); S5 = S5 + tabC(i,11); else tabD = [tabD; [O D L U avg H S1 S2 S3 S4 S5 ] ];

O = tabC(i,1); D = tabC(i,2); L = tabC(i,3); U = tabC(i,4); avg = tabC(i,5); H = tabC(i,6);

S1 = tabC(i,7); S2 = tabC(i,8); S3 = tabC(i,9); S4 = tabC(i,10); S5 = tabC(i,11); end end

% DELETE UNCECESSARY COLUMNS tabE = tabD; tabE(:,4) = []; tabE(:,4) = []; tabE(:,4) = [];

% COMBINE UP/DOWN DATA tabF = []; for i=1:1:length(tabE) O = tabE(i,1); D = tabE(i,2); L = tabE(i,3);

S1 = tabE(i,4); S2 = tabE(i,5); S3 = tabE(i,6); S4 = tabE(i,7); S5 = tabE(i,8);

% Check if link is already added

linkCreated = 0; for j=1:1:size(tabF,1) if ( tabF(j,1) == D && tabF(j,2) == O ) linkCreated = 1; break; end end

pairedLink = 0;

% Create link only if it is not added

if (linkCreated == 0)

% Check for paired link for j=1:1:length(tabE) if ( tabE(j,1) == D && tabE(j,2) == O )

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tabF = [tabF; [O D L S1 tabE(j,4) S2 tabE(j,5) S3 tabE(j,6) S4 tabE(j,7) S5 tabE(j,8)]]; pairedLink = 1; end end

% Otherwise unpaired if (pairedLink == 0) tabF = [tabF; [O D L S1 0 S2 0 S3 0 S4 0 S5 0]]; end end end

% GET VELOCITY tabJ = tabF; for i=4:1:13 v = tabF(:,3) ./ tabF(:,i) * 1.09728; %if ( v ~= Inf) tabJ(:,i) = v; %end end for i=1:length(tabJ) for j=1:13 if ( tabJ(i,j) == Inf ) tabJ(i,j) = 0; end end end matrix2latex(tabJ, 'tab.tex', 'alignment', 'l','format', '%0.0f') xlswrite('mytable', tabJ)

% AVERAGE UP-DOWN VELOCITY

%tabG = tabF; tabG = tabJ; for i=1:1:length(tabG) for j=4:2:12

% If Both Up and Down data available if( tabG(i,j) ~= 0 && tabG(i, j+1) ~= 0 ) tabG(i,j) = ( tabG(i,j) + tabG(i, j+1) ) / 2; else % Ony Up or Down data available tabG(i,j) = tabG(i,j) + tabG(i, j+1) ; end end end

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tabH = tabG; tabH(:,[5 7 9 11 13]) = [];

% UPDATE TABLE BY ADDING MISSING DATA edges = csvread('edges.txt'); edges = [edges; dtMissing(:, [1 2 3])]; dtMissingVel = dtMissing; for i=4:1:8 vel = dtMissing(:,3) ./ dtMissing(:,i); dtMissingVel(:,i) = vel; end for i=1:length(dtMissingVel) for j=1:8 if ( dtMissingVel(i,j) == Inf ) dtMissingVel(i,j) = 0; end end end tabH = [tabH; dtMissingVel];

%run('maps') clc

% GENERATE HEATMAP AND CONTOUR MAP % ======

% Set speed limit for heatmap in km/hr v1 = 10; % km/hr v2 = 20; % km/hr

% (v1) (v2) % 10 km/hr 20 km/hr % <------|------|------> % Low Medium High

% Set source node for contour source = 48; dest = 37; times = []; paths = [];

% Set contour interval in minutes interval = 10;

% Google Map on/off

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gmap = 'on'; addpath('graph'); roadnetwork(nodes, edges, 'Fig--NETWORK', gmap); for j=4:1:7 O = tabH(:,1); D = tabH(:,2); L = tabH(:,3); V = tabH(:,j); T = (( L ./ 3280) ./ V) * 60;

i = find(V(:,1) == 0 | V(:,1) > 120); O(i,:) = []; D(i,:) = []; L(i,:) = []; V(i,:) = []; T(i,:) = [];

if(length(O) ~= 0 ) filename = strcat('Fig--HEAT MAP--Time Range-- ', num2str(j-3)); heatmap(O, D, V, v1, v2, nodes, filename, gmap );

filename = strcat('Fig--MINIMUM PATH--Time Range-- ', num2str(j- 3)); %[time path] = min_time(O, D, T, nodes, source, dest, filename, gmap); min_time(O, D, T, nodes, source, dest, filename, gmap);

filename = strcat('Fig--CONTOUR MAP--Time Range-- ', num2str(j- 3)); contour(O, D, T, nodes, source, interval, filename, gmap );

end end

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