NED UNIVERSITY OF ENGINEERING AND TECHNOLOGY, KARACHI-75270 DEPARTMENT OF URBAN AND INFRASTRUCTURE ENGINEERING

Chairman/PI Prof. Dr. Mir Shabbar Ali Professor (Transportation Engineering)

Phone: (92-21) 9261261-8 Ext 2354 Fax: (92-21) 9261255 Email: [email protected] [email protected] http://www.neduet.edu.pk/UE/index.htm

Dated: July 4th , 2016 Ms. Afifa Irshad Dy. Director, NRPU(R&D) Higher Education Commission H-9 Islamabad, Pakistan Email: [email protected]

Subject: FIRST YEAR PROGRESS REPORT National Research Program for Universities (NRPU) RESEARCH PROJECT Prediction of Traffic Congestion in Karachi Metropolis using Artificial Intelligence Techniques

Please find enclosed FIRST YEAR report submitted for National Research Program for Universities (NRPU) RESEARCH PROJECT Prediction of Traffic Congestion in Karachi Metropolis using Artificial Intelligence Techniques (title page 2).

The duration of the project is of two years, w.e.f. 1st May 2015. HEC allocated total of Rs. 3,703,000 for this two years research project, out of which Rs. 2,139,000 is already received as Year 1 layout (please see page 3), and being utilized within their respective heads, while Rs. 1,564,000 is to be disbursed in the second year of research.

A separate account is being maintained by DF-NEDUET and all disbursements are carried out with the approvals of VC under advice from Resident Auditor, NEDUET. This channel ensures all fund utilization to be within HEC earmarked heads as well as following SPPRA rules and regulations. The major heads of fund utilization are provided on page 4.

Submitted for your perusal and necessary action at your end and requested for release of second trench of Rs. 1,564,000 to enable completion of the research activities.

Prof. Dr Mir Shabbar Ali Enclosures: a) Original Budget Utilization Report

Copy to: 1. Dean (CEA) 2) DF Page ii of 123

Page iii of 123

National Research Program for Universities (NRPU) Prediction of Traffic Congestion in Karachi Metropolis using Artificial Intelligence Techniques

First Year Progress Report May 2015 – June 2016

Researchers: Prof. Dr. Mir Shabbar Ali Dr. Sana Muqeem Professor / PI* Assistant Professor / Co-PI* Execution agency: *Department of Urban and Infrastructure Engineering NED University of Engineering and Technology, Karachi, Pakistan Funding Agency: Higher Education Commission of Pakistan, HEC, Islamabad

Page iv of 123

Contents National Research Program for Universities (NRPU) ...... iv SECTION 1. EXECUTIVE SUMMARY ...... 1 1.1. Introduction ...... 1 1.2. Background...... 2 1.3. Current State of Research ...... 5 1.4. Scope and Objectives ...... 5 1.5. Methodology ...... 6 1.6. Summary of progress to date ...... 7 1.7. Anticipated deliverables and time line...... 7 1.8. Collaborations established ...... 8 1.9. Research outputs ...... 8 SECTION 2. RESEARCH PROGRESS ...... 10 2.1. Literature Review ...... 10 2.1.1. Traffic congestion as a major civic problem ...... 10 2.1.2. Previous studies on traffic congestion issues...... 12 2.1.3. Recovering from congestion: ...... 12 2.1.4. Causes of Congestion ...... 13 2.1.5. Traffic congestion modeling techniques ...... 21 2.1.6. Artificial Intelligence (AI) application in traffic congestion modeling ...... 21 2.1.7. Fuzzy Logic ...... 22 2.2. Expert Opinions Survey ...... 22 2.2.1. Questionnaire development ...... 22 2.2.2. Pilot Survey ...... 23 2.2.3. Identifying Experts and Conducting Interviews ...... 24 2.2.4. Factors prioritization ...... 24 2.3. Arterials Selection for Study and Pilot Survey ...... 24 2.3.1. Categorization of Factors ...... 26 2.3.2. Further Categorization of Factors ...... 26 2.3.3. Floating Car Method on University Road ...... 27 2.4. Field Data Collection ...... 28 2.4.1. Identifying Congestion Hotspots Using Google Maps ...... 28 2.4.2. Consulting Traffic Police ...... 28 2.4.3. Preparing Pro formas for City-wide Data Collection ...... 28 2.4.4. Field Surveys for Identifying Pavement Condition and Encroachment Levels ...... 29 2.4.5. Survey and Analysis of Pavement Condition Effects on Traffic Congestion ...... 29

Page v of 123

2.5. Traffic surveillance for capacity assessments at bottlenecks ...... 30 2.5.1. Data Extraction from Traffic Videos on Rashid Minhas Road ...... 30 2.6. Tasks in progress ...... 34 2.6.1. Fuzzy Logic Model ...... 34 2.6.2 Data Preparation for Fuzzy Logic Model ...... 44 2.6.3. Field Surveys of Congestion Hotspots ...... 51 2.7. Further tasks ...... 51 2.8. Fund utilization ...... 51 2.8.1. Research staff ...... 52 2.8.2 Equipment ...... 52 2.8.3 Expendable supplies ...... 52 2.8.4 Publications ...... 53 SECTION 3. AUXILIARY RESEARCH PROJECTS ...... 54 3.1. Correlation between Driver Behavior and Traffic Heterogeneity ...... 54 3.2. Effect of pavement conditions on travel speed ...... 56 3.3. Capacity of U-Turn near Aladdin Park (FYP) ...... 58 SECTION 4. APPENDICES ...... 61 Appendix A: Expert Opinion Form for Causes of Traffic Congestion ...... 62 Appendix B: Survey Form for Congestion on Arterials ...... 66 Appendix C: Map of Selected Arterials of Karachi ...... 70 Appendix D: Congestion Chart ...... 73 Appendix E: Plan for Recording Traffic Videos at Selected Locations and Times ...... 79 Appendix F: Pro formas ...... 81 Appendix G: Relative Importance Index for Prioritizing Factors ...... 84 Appendix H: Encroachment and Pavement Condition Data at Selected Locations ...... 85 Appendix I: Number and Width of Lanes of Selected Roads (Static Factors) ...... 92 Appendix J: Land Use (Static Factors) ...... 102 Appendix K: Driver Behavior (Dynamic Factors) ...... 108 Appendix L: Traffic Counts ...... 110 Appendix M: Speed Observations for University Road...... 113 Appendix N: Financial Statement ...... 116

Page vi of 123

SECTION 1. EXECUTIVE SUMMARY

1.1. Introduction Our research project, titled “Prediction of Traffic Congestion in Karachi Metropolis through Artificial Intelligence Techniques”, is intended to fill the void in existing research based on traffic prediction, with a focus on Karachi‟s indigenous traffic conditions. Congestion studies attribute the causes of highway congestion to factors known as triggers and drivers, many of which are qualitative in nature. Computer models that have so far attempted to predict traffic congestion have not been able to accurately represent these qualitative factors, as a result of which the prediction is inaccurate and unreliable. Our research utilizes Artificial Intelligence (AI), a branch of computing that is especially designed to mimic real life and perform calculations on imprecise and non-discrete phenomena. We are therefore able to factor in some of the most direct qualitative causes of congestion, such as abrupt lane changing and aggressive driving, in our model, giving it a much higher degree of accuracy.

Using an expert system to identify and prioritize congestion causes, we will then proceed to gather information on these causes in real-time conditions. Our research will incorporate visual observation of traffic streams for information on congestion triggers and drivers prevalent in a selected roadway stretch. This will be accomplished by CCTV cameras and auxiliary equipment such as digital video recorders. We will also carry out traffic studies near areas of pavement damage, since these are important congestion drivers. After studying the impact of specific types of pavement damage and other observable factors through spot speed and flow measurements, we will use the Fuzzy Logic Toolbox in MATLAB R2009a to formulate a congestion prediction model. Using a series of if-then rules, we will be able to predict congestion severity and location on the basis of inputs that are both qualitative (such as pavement condition) and quantitative (such as traffic flow). Through comparison with a Multiple Linear Regression model, we will obtain the statistical accuracy of our model.

According to an earlier research project titled „Quantification of Traffic Congestion Cost‟ (conducted through collaboration with NED University‟s Urban Engineering department and Indus Motors Pvt. Ltd.), the total cost due to congestion in Karachi is approximately Rs. 131.7 million ($1.34 million) per day. This was calculated through determining the amount of time lost by each person stuck in traffic jams, and multiplying it by the time value of money (dollars or rupees per hour) for each person. Congestion also has myriad negative effects on the environment, health and aesthetics of a city. By predicting the location and intensity of a traffic jam, timely efforts may be made to reallocate traffic to alternative routes so that exacerbation of the congestion may be averted.

Page 1 of 123

This document is divided into 4 sections. Section 1 is a summary of our project and how it will be undertaken. Section 2 is the progress report, detailing what has been accomplished so far. Section 3 describes transportation research projects that are taking place side by side with this project in our department. Section 4 is a list of appendices that contain data collection forms (pro formas) the data we have gathered over the course of the project.

1.2. Background Pakistan is a developing country and many developing projects like shopping malls, commercial and residential towers (+25 stories) have either been completed recently or under construction in its cities. Due to these rapid construction activities, the traffic network needs improvement. Road traffic congestion is a critical problem accelerated by an exponential increase in the number of vehicles and a high level of urbanization. Optimal utilization of the existing infrastructure can effectively reduce the congestion levels without the necessity of constructing newer infrastructure to accommodate the increased traffic volume (Srinivasan et al., 2006).

Karachi is the largest city of Pakistan, having a population of approximately 20,000,000. It is the economic hub of the country with an international airport named “Jinnah International Airport” and two sea ports named “Karachi Port” and “Port Bin Qasim”. It has a complex traffic network which connects commercial and residential zones of the city, which cover an area of 3527 km2. The total road network in Pakistan was measured to be 258,350 km in 2009. According to the Asian Development Bank, the number of private motor vehicles in Karachi is growing by 9% per year, and this adds 280 vehicles every day, leading to immense traffic congestion and causing time loss, economic loss and health hazards. Time loss includes the delay in travel time, while increasing fuel usage and vehicle maintenance costs hit citizens economically. Furthermore, air pollution and noise pollution cause health hazards. These factors negatively affect the country‟s economy and the lifestyle of its citizens. Therefore, it is necessary to have a traffic congestion model to predict travel time delay, reflecting the influencing factors in a particular link for controlling and managing the traffic in an efficient way.

It was found that traffic congestion cost of Karachi in 2013 is 688 million USD per year and it is 2% of the total revenue of Pakistan. For an urban city of developing countries, traffic congestion cost may be around 1-2% of the GDP that particular city is contributing (M.S. Ali et al., 2013). Moreover, the urban areas of Karachi experience more traffic volumes as compared to industrial areas. From the time based volumes of an urban highway in Karachi, we can see that the peaks in both personal travel and transport of goods occur between 9 a.m. till 7 p.m.

Page 2 of 123

Port Qasim to Pakistan Steel

21:00 19:00 17:00 15:00 13:00 Total Persons 11:00 9:00 7:00 0 2000 4000 6000 8000 10000 12000

Quaidabad to Mazil Pump

21:00 19:00 17:00 15:00 13:00 Total Persons 11:00 9:00 7:00 0 5000 10000 15000 20000 25000

Fast Uni to Port Qasim

21:00 19:00 17:00 15:00 13:00 Total Persons 11:00 9:00 7:00 0 5000 10000 15000 20000 25000

Mazil Pump to Fast Uni

21:00 19:00 17:00 15:00 13:00 Total Persons 11:00 9:00 7:00 0 5000 10000 15000 20000 25000

Figure 1.2.1: A time-based comparison of four stretches of Shahra- e-Faisal with respect to total persons traveling. Page 3 of 123

Fig 1.2.2: A time-based comparison of four stretches of Shahra-e- Faisal with respect to total persons traveling.

Page 4 of 123

1.3. Current State of Research Although several congestion models have been developed (Lindsey et al. 1999), the overwhelming majority of them have focused on the quantitative causes of congestion. It is well known that several qualitative factors such as driver behavior, ease in buying vehicles, pavement condition and vehicle heterogeneity are either triggers or drivers for congestion. By excluding these from a congestion model, the accuracy and applicability of the model suffers. Although a multivariable approach may be used to bring some of these factors close to a discrete value, this is both time-consuming and difficult to reproduce for models in different regions.

Secondly, artificial intelligence has not been used for calculations in these models. The advantage of using AI is that it can process quantitative and qualitative factors more accurately and can also learn calculation processes (such as through neural networks). This allows the model to be iteratively improved until a desired level of accuracy is achieved. This is ideal for a congestion model, where input values can change rapidly and unexpected trends in traffic behavior are common (such as during periods of inclement weather, rallies or public gatherings, or VIP movement). By using AI techniques, our model can be comprehensive, incorporate many inputs while allowing the easy addition of new variables, and can be quickly adapted for new regions.

1.4. Scope and Objectives Our primary objective is to develop a comprehensive congestion prediction model for urban networks that successfully incorporates qualitative and quantitative congestion causes and accurately simulates their effects through fuzzy logic. This will remedy the main shortcoming of existing models, namely, their failure to accurately capture the effect of qualitative congestion causes and perform accurate calculations on dynamic inputs. Although we have chosen the road network of Karachi as our research area, we anticipate that our model will be equally accurate when applied to urban networks of similar magnitude and complexity.

Dissemination of the output of this project will be carried out through Karachi Metropolitan Corporation (KMC), Transport Planners/ Traffic Engineering consultants. The output of the project will be to determine and predict the ideal free flow speed without delay with minimum influences of adverse qualitative and quantitative variables. The project will identify the most critical variable (qualitative or quantitative) which results in maximum traffic congestion and suggested to be improved. For example, if at specific corridor pavement condition is highlighted (through modeling) as most severe variable causing delay in the traffic, and with the maintenance of pavement condition the severe traffic congestion can be reduced for that specific corridor. The relevant department of KMC will be approached and advised to improve the pavement conditions.

Page 5 of 123

1.5. Methodology Phase I of this research includes an extensive literature review through which use of Artificial Intelligence techniques in the field of transportation engineering, especially for prediction purposes are explored. In our case, the Fuzzy Logic toolbox of MATLAB (R2009a) is selected to develop a prediction model. This tool creates input space to an output space through a mechanism of if-then rules. For the development of model, it is very necessary to understand the factors on which traffic congestion depend and how will it be used in fuzzy logic to achieve a model of the desired accuracy. For this purpose the literature review is divided into two parts; part one is focused on obtaining the qualitative and quantitative factors with their impact on traffic congestion, and part two is focused on software exploration: how MATLAB works using fuzzy logic tool to understand the mechanisms and the theory behind the fuzzy logic tool.

The qualitative and quantitative factors that affect traffic congestion are identified and prioritized to assess their impact on traffic congestion.

Quantitative Factors i. Lane width ii. No. of lanes iii. Traffic composition iv. Population growth v. Travel speed vi. Traffic volume vii Road capacity

Qualitative Factors i. Pavement condition ii. Type of land use (residential, commercial, industrial) iii. Bus stop availability iv. Weather condition (rainfall) v. Driving behavior (tolerance level/aggression level)

Page 6 of 123 vi. Presence of road intersection at small intervals (approximately 0.5 km) vii. On-street parking

In quantitative parameters, lane width and no. of lanes reflect road capacity. Travel time, traffic volume and traffic composition reflect traffic characteristics. Population growth rate incorporates future usage of road intersection.

In qualitative parameters, psychological factors include driving behavior, while land use determines the level of interruption in traffic flow due to on-street parking and presence of hawkers along road sides. Road surface condition and presence of road intersections reflects planning and regulation work.

1.6. Summary of progress to date We have completed our literature review on congestion and its causes, artificial intelligence and Karachi‟s main arterials. We conducted an expert opinion survey for identifying and prioritizing causes of congestion (Appendix A), and divided the identified causes into static and dynamic factors. We then selected arterials in Karachi for our study – University Road, Shahra-e-Faisal, Sher Shah Suri Road and Nawab Ali Siddique Khan Road, Shahra-e-Pakistan, Jamshed Road and M. A. Jinnah Road, and Rashid Minhas Road (Appendix B and C). We conducted a pilot survey of the congestion levels on University Road through floating car technique and collected speed and pavement condition data (Appendix M), and collected traffic videos for one whole day on three locations on Rashid Minhas Road. We later used these videos for a pilot study on driver behavior and vehicle counting (Appendix K and L). Using Google Maps, we identified areas and times during which congestion will be highest on the selected arterials (Appendix D). We also collected encroachment data and pavement condition data for the identified sections on Rashid Minhas Road and University Road (Appendix H). Using Google Earth, we made a land use map for the selected arterials (Appendix J).

1.7. Anticipated deliverables and time line This research will yield valuable data on the arterials we have selected. We already have a congestion map showing the times and locations of congestion on these arterials. We plan to collect complete data on pavement condition, encroachment, bottlenecks and the other identified factors for these arterials. Other than the congestion model, we will also have collected data on driver behavior, vehicular mix and traffic flows upon the completion of our project, which will be in July 2017.

Page 7 of 123

1.8. Collaborations established In line with the research proposal submitted and accepted by HEC, this research is enabling stakeholders to benefit from the expertise and vision of the research team and the research outputs to date.

One of a remarkable illustration is the utilization of real time traffic updates map by DIG traffic in their command and control centre established in Karachi, the idea of which was shared by the research team. Secondly, a WhatsApp group has been established to provide live traffic updates by DIG traffic office in which our research team member provides active inputs. Thirdly, this research is benefited directly from various traffic posts established in Karachi, in the form of their inputs in identification and confirmation of traffic congestion locations.

1.9. Research outputs In terms of research outputs, the first year of the research has been able to produce three research paper drafts, one final year project and has identified five masters research projects which will be started in the fall 2016 semester at NED University.

Page 8 of 123

24 23

22

21

20 ANNEXURE-1

19

18 YEAR 2 YEAR

17 16

15

14 13

12

TIMELINE TIMELINE 11

10 9

8 7

6 YEAR 1 YEAR

5 4

3 2

1

ANNEXURE-1

Project MonthsProject

Recommendation for future research based on the results obtain obtain results the on based research future for Recommendation

Mitigation measures for factors having high severity impacts on traffic congestion traffic on impacts severity high having factors for measures Mitigation

Dissimination of results and collaboration efforts collaboration and results of Dissimination

Sensitivity Analysis to identify individual impact of each factor on Congestion on factor each of impact individual identify to Analysis Sensitivity

Phase IV (CONCLUSIONS & RECOMMENDATION) & (CONCLUSIONS IV Phase

Selection of Best Model among MLR and FL and MLR among Model Best of Selection

Comparison of the results of MLR with FL model FL with MLR of results the of Comparison

Development of Multiple Linear Regression Model (MLR) Model Regression Linear Multiple of Development

Phase III (COMPARISON WITH STATISTICAL TECHNIQUES) STATISTICAL WITH (COMPARISON III Phase

Selection of Best Model among Trial 1 and Trail 2 Trail 1 and Trial among Model Best of Selection

Calculation of Mean Square Error Square Mean of Calculation

Development of Fuzzy Logic Model; FL (Trial 1 and Trial 2) using MATLAB using 2) Trial 1 and (Trial FL Model; Logic Fuzzy of Development

Data Preparation for model (Input and Output Variables) Output and (Input model for Preparation Data

Phase II (DEVELOPMENT OF A.I BASE PREDICTION MODEL) PREDICTION BASE A.I OF (DEVELOPMENT II Phase

Understatnding of local models, Lahore MBS visit MBS Lahore models, local of Understatnding

Data Analysis Data

Field Observation for Data Collection Data for Observation Field

Prioritizing the Qualitative and Quantitative Factors Quantitative and Qualitative the Prioritizing

Literature Review Literature

PHASE I PHASE TASKS

Fig. 1.7.1: Project Timeline

Page 9 of 123

SECTION 2. RESEARCH PROGRESS

2.1. Literature Review

2.1.1. Traffic congestion as a major civic problem Traffic congestion is variable in its description, since it is closely linked to the Level of Service, which itself depends on diverse user opinions. For the purpose of simplicity, it can be defined in three different ways.

i. The more complete definition of excessive congestion is “when the marginal costs of congestion to society exceed the marginal costs of efforts to reduce congestion.”1 In other words, when the cost of congestion (due to wastage of time and pollution due to idling vehicles) is higher than the cost of widening roads and implementing other congestion-reducing measures, congestion can be termed “excessive”. ii. Congestion can be said to arise when the general flow of a roadway exceeds its dynamic capacity. The dynamic capacity is set by the interaction of vehicle types and lengths, traffic speeds, ingress and egress patterns, lane switching and car following behavior, and is influenced by the atmospheric and road conditions. The variable nature of dynamic capacity makes it a much more realistic and useful descriptor of roadway capacity, since studies have consistently shown that roadway capacities become unpredictable as traffic flows change from “decreasing speed, increasing flow” to “decreasing speed, decreasing flow”2. This occurs at the apex of the curve shown below.

Fig. 2.1.1.1: Speed Flow Curve for Uninterrupted Highways3 iii. A shorter and more practicable description of congestion is “when the throughput of a roadway is decreasing despite decreasing vehicular speed.” Roads are designed to serve a maximum number of users, and as this number increases, the average speed at which users traverse the facility is sacrificed. However, when the speed as well as the throughput suffers due to the traffic level, and the economic benefit of building the facility is reduced, the facility can be considered “congested.”

Page 10 of 123

Traffic congestion is a problem that has plagued developed and developing countries alike, which often leads to the perception that it is an unavoidable outcome of a growing population and economy. Increasing the capacity of roads and mass transit often provides only a temporary solution: despite an extensive network and high ridership in New York‟s subway system, rush hour congestion in the city is very high. There is no doubt, however, that decongestion measures such as car parks, mass transit and pedestrian/cyclist friendly cityscapes help take several vehicles off the road. Example

Congestion harms the environment in numerous ways. Vehicles waste fuel while idling, thereby contributing to global warming and depletion of fossil fuels. Vehicular emissions cause acid rain, smog, discoloration of urban structures and several diseases in humans such as respiratory problems, cancer, and stress. Although it is often argued that a lowered speed due to congestion reduces the severity of accidents, it has often been observed that drivers speed up after escaping from a congestion hotspot, which increases the risk of accidents. Roads also deteriorate prematurely, since they are not designed to accommodate extremely slow moving vehicles. Vehicles also contribute the heat island effect, especially while they are stuck in a traffic jam.

Congestion is a direct outcome of not just urban sprawl but also the ideal of a car and a wide road close to one‟s residence. By designing a city in such a way that motorized vehicles become indispensable to transport, congestion and pollution become inevitable. Any congestion mitigation strategies that free up road space temporarily will soon be overwhelmed by induced demand. While it is commonly agreed upon that it is virtually impossible to significantly and permanently reduce congestion, planning the city in a way that reduces the dependency on private vehicles is the most important requirement for preventing congestion.

Sources:

1. Adapted from VCEC (2006), p. xvi. 2. http://www.internationaltransportforum.org/pub/pdf/07congestion.pdf 3. ECMT (2007)

Page 11 of 123

2.1.2. Previous studies on traffic congestion issues One of the most comprehensive documents on traffic congestion is a report titled „Managing Urban Traffic Congestion‟, published by the Transport Research Centre. This is a joint project of two international organizations, the Organization for Economic Co-Operation and Development and the European Conference of Ministers of Transport. Among the topics covered in this report are

1. Defining congestion 2. Causes of congestion 3. Assessment and measurement of congestion 4. Congestion response and mitigation strategies

This report, and the research papers referenced therein, described the following patterns, observations and ramifications of congestion:

1. Traffic congestion is an inevitable outcome of economic and population growth 2. Because roads are not designed to be used at free-flow speeds, it is erroneous to assume that time is wasted because of reductions in speed 3. It is impossible to significantly and permanently reduce congestion 4. Congestion on interrupted and uninterrupted links is caused by different factors 5. Induced demand means that any reductions in congestion are temporary 6. It is necessary to bring congestion down to a manageable level to avoid extreme environmental degradation. Some mitigation strategies include car parks, mass transit and congestion pricing.

2.1.3. Recovering from congestion: Congestion and subsequent recovery is known to follow hysteretic behavior. This means that the relationship between the cause and effect is such that reversing the cause by a certain amount does not reverse the effect by the same amount. When the flow of traffic breaks down from B to D as shown in the figure above, the change is sudden and temporary. In order for the flow to recover, the traffic density must be lowered significantly and not just to the density at point B. This much lower density will allow the vehicles to accelerate fast enough away from the congested area so that recovery can begin. Therefore, a failure to provide this low density can prolong existing congestion. The figure below illustrates hysteretic loading and recovery.

Page 12 of 123

Fig. 2.1.3.1: 2- and 3-Phase Flow-Density Diagrams (Adapted from Maerivoet, S. and de Moor, B. (2006)

2.1.4. Causes of Congestion The causes of traffic congestion can be categorized as triggers, drivers and random factors.

Triggers are micro-level actions that are the most immediate and direct cause of congestion. Examples include bottlenecks, sudden changing of lanes or rapid deceleration. Triggers can be readily identified or measured.

Drivers are macro-level conditions that originate from the demand for transportation. Examples include increasing population, car ownership and dependency, and availability/cost of parking. Drivers contribute to the incidence of congestion and its severity. They also include exogenous factors such as second-order demand and trip patterns and volumes.

Random factors are those related to largely unforeseen events such as weather and poor visibility. They are not very important since there are ways to account for their effects while planning for congestion, based on the likelihood of their occurrence and severity. That is not to say that their effects are not important8, rather, they can be accounted for even though they inherently unplanned.

How does congestion occur in uninterrupted links?

Congestion occurs due to a convergence of circumstances. The same triggers that bring about the congestion may have been occurring before in free flow conditions without causing congestion. Similarly, even when the roadway demand has equaled or exceeded its capacity, congestion may still

Page 13 of 123 be avoided; however, it is also possible for congestion to occur before the demand equals the capacity (for example, due to a vehicular collision).

Traffic congestion utilizes the concept of dynamic capacity rather than traditional concepts of fixed capacity. Now, as demand changes, so can the capacity of a roadway. As a result, the relationship between demand and capacity becomes probabilistic rather than deterministic.

To simplify, we can say that congestion occurs when incidents such as lane changing, following distance fluctuation or vehicular collisions result in a transition from decreasing speed and increasing throughput, to decreasing speed and decreasing throughput.

Congestion can be recurrent or non-recurrent. Recurrent congestion can be due to rush hour traffic or weekend trips, and is clearly the less worrisome of the two, since travellers can adapt to it and change their trips accordingly. Non-recurrent congestion can be due to aberrant weather or road works, and accounts for around 55% of all congestion3. However, by planning for these delays through congestion management policies, this can be brought down to 14 – 25%4.

Triggers on uninterrupted links:

The following are known to cause sudden, temporary changes in throughput capacity of an uninterrupted roadway:

o Car following behavior (distance and gap choices) o Speed choice and differential speeds o Acceleration and/or deceleration o Lane-changing behavior

The moment at which congestion will be triggered can be determined by the sequence and mix of4:

 Vehicle types  Driver types (risk prone, risk averse, aggressive)  Information level of drivers (familiarity with route, congestion expectancy etc.)  Trip purposes  Driver moods

There are 4 major types of bottlenecks on roadways5:

1. Visual effects for drivers, such as a. Roadside distractions b. Rubbernecking

Page 14 of 123

2. Abrupt changes in highway alignment, such as a. Sharp curves b. Hills c. Work zones 3. Intended interruptions to flow, such as a. Signals b. Tollbooths 4. Vehicle merging maneuvers, such as a. Lane drop (lane is lost) at bridge crossings and work zones b. Crashes and debris c. Vehicles having to weave through traffic to enter and exit d. Freeway interchanges/ramps e. Micro-bottlenecks due to lane changing and speed differentials

Congestion triggers on interrupted-flow facilities:

While motorways and other signal-free corridors have their congestion defined using flow and dynamic capacity, on urban roads with signalized intersections congestion can simply be quantified as delay. Hence, anything that delays the movement of traffic on these roads acts as a trigger.

In urban roads, the link capacity is less important in determining congestion than intersection capacity, even though it may be much more than the latter. The intersection capacity depends on the physical and operating characteristics of the incoming and outgoing links, as well as the geometric design of the intersection (such as left-turn lanes for left-handed cars) and on-street parking configuration at or near the intersection.

At the intersection itself, the driver behavior is affected by:

a. Built environment b. Signage c. View sheds d. Geometric disposition of intersection

The most important trigger by far is the traffic signal itself. Poorly coordinated traffic signals are the biggest cause of urban congestion. Other important factors that can cause delays include:

 Parking maneuvers  Delivery traffic such as bus stops

Page 15 of 123

 Turning movements

On the unsignalized intersections between major and minor streets, queues can form which lead to delay. Gap sizes between vehicles depend on the types of maneuvers (left, right, through), number of lanes, and the speed of major street vehicles and sight distances.

Congestion drivers:

The demand for transportation is the basic cause for all congestion. This demand can come from a number of factors:

1. Social and economic growth. 2. Increasing population 3. Car ownership and dependency 4. Land uses 5. Travel patterns 6. Public transport options 7. Urban freight transport and goods delivery 8. Parking

A study of these factors in the milieu of congestion illustrates that vehicles and the congestion that they cause are not just influenced by the urban environment, but they also shape the urban character around them. The relationship is bidirectional. For example, one reason why people buy cars could be that activity centers are spaced too far apart. Eventually, future activity centers will be spaced apart to avoid the congestion resulting from the influx of new cars, while roads will become noisy and the environment will become polluted. Just as the decision to buy a car was influenced by socio- economic factors, the environment and the urban layout, buying a car and using it will affect the same factors as well.

The main stimulants for car ownership can therefore be identified as:

a. Population increase b. Personal income growth c. Workforce habits or requirements (if telecommuting jobs are replaced with those requiring frequent travel, such as that of salesmen, congestion will occur) d. Complex urban mobility patterns e. Urban growth in suburbs (small roads not being equipped to handle traffic)

Page 16 of 123

f. Underpricing of infrastructure (where people do not pay for the facilities that they use and the congestion that they cause) g. Planning and investment practices (if public funds and planning does not go into improving roads and parking to improve congestion, more people will buy cars)

An increase in cars, coupled with the limitations in road infrastructure (roads can only be cost- effectively widened up to a certain degree) have long been ascribed as the main causes of urban congestion, due to the low load factors of cars as opposed to buses.

Land use:

Land use and its effect on congestion remains unclear partly due to contradictory findings regarding complex land use patterns. While intuition suggests that complex trip patterns that arise due to mixed use will increase congestion, mixed use also shortens trips, allowing for walking or cycling to accomplish the same tasks as cars previously did. Aggregating activities in an urban space increases congestion but decreases transport costs, thereby offsetting some of the cost of congestion.

The spatial imprint of transport facilities (the amount of space they take up, in the form of parking areas, operation routes and depots) on limited urban land, and the role of land use in attracting, limiting or aggregating trips in certain parts certainly has important repercussions for congestion.

Travel patterns and public transport:

Another result of land use, travel patterns are also drivers of congestion, since they help perpetuate recurrent congestion. They include:

1. Daily commuting trips (cyclic, predictable and recurring) 2. School runs (can lead to congestion if private vehicles are used instead of school buses) 3. Professional activity trips (such as meetings and customer services) 4. Personal trips, such as shopping 5. Tourist trips, which are seasonal 6. Freight

Travel that is recurring is particularly problematic because roadways and other transport facilities cannot always be at their peak operational capacity, leading to an exacerbation of recurrent, predictable congestion into unpredictable and severe congestion that spreads into other modes of travel.

For example, if a few buses are removed from the fleet, all the remaining buses will take longer to arrive and will be more loaded with passengers than usual. As a result, passengers will be

Page 17 of 123 dissatisfied with the bus service, and may consider switching to other modes of travel, such as private vehicles. This will increase congestion even more. Furthermore, public transport corridors are often congested due to induced demand. It is imperative that policies for traffic management take into account the induced demand and plan for reduced travel time despite the influx of additional traffic.

Urban freight transport and goods delivery:

Large vehicles used for delivering freight are not just moving roadblocks that take up a lot of space; they are also difficult and slower to maneuver. The various factors that have to be considered with regard to the congestion caused by these vehicles are as follows:

a. If the customer is not home when the delivery vehicle arrives, or if the customer is dissatisfied upon delivery and returns the item without paying, then the whole trip is wasted. Rates of success are therefore inversely proportional to congestion b. Drop density of home delivery rounds (the number of customers served in one delivery round) – a higher density increases trip efficiency and may decrease congestion c. Whether the home delivery costs are truly reflected in the bill. If the delivery price is added to the selling price, the customer does not see it as an extra, and may order more items, increasing congestion d. Whether delivery systems will require regular trips to render a service, such as replacing the filter cartridges for a water purification system. This will increase congestion e. Whether there is a parking area outside the customer‟s home. Searching for parking may increase congestion f. Delivery time constraints imposed by customers or authorities. Aggregating trips in a certain time slot may increase congestion g. Location of depot

Private vehicles and the search for parking:

As mentioned above, when vehicles are looking for parking despite reaching their destination, they are causing needless congestion for the other vehicles using the roadway. According to a study in Copenhagen, longer trips are usually associated with longer times spent looking for parking.

Page 18 of 123

Fig. 2.1.4.1: Time Spent Searching for Parking in Copenhagen10

Induced demand:

The interdependent nature of many areas of traffic often makes it difficult to conclusively identify ways to solve problems such as congestion. Nowhere is this made more apparent than by the phenomena of induced demand.

Induced demand is the demand created simply by virtue of the creation of the new transportation facility. Many users will want to ride on a newly introduced roadway, subway or BRT simply to experience the new facility. Many people will buy a car simply because of the creation of a new road near them. This is different from latent demand, which are the trips that are only “waiting to be made”, and are being withheld due to limitations in existing infrastructure.

Although latent demand may be gauged more readily, induced demand is only apparent after the facility has been made. Indeed, it is one of the reasons why adding capacity to a roadway does not reduce congestion as much as planned. According to several studies, increasing capacity or travel speed will result in increases in volume over the short and long term. However, increasing capacity will benefit existing road facilities at least initially, and is therefore justified.

Page 19 of 123

Fig. 2.1.4.2: Summary of Representative Estimates of Traffic Volume Elasticities11

On the other hand, reducing capacity does not always increase congestion, granted that users are able to switch to another facility or mode6, 7. Although counterintuitive, this serves to illustrate the flexible and diverse nature of user responses to traffic management measures. It is essential that planners do not think of induced demand as finite or temporary, and that they anticipate unexpected user responses to new schemes and traffic management projects. As land use, demographic and socioeconomic factors determine activity patterns, which in turn impact travel behaviors of individuals, households and firms, which give rise to travel demand, which ultimately shapes the dynamic capacity, only the most in-depth planning will yield a facility that truly anticipates congestion. While the first highway built between two cities will be the most cost effective and will bring a windfall in economic benefits, subsequent efforts are likely to yield decreasing benefits to users, and may in fact benefit a completely different kind of user than the ones intended (for example, travelers who have adjusted to congestion and have planned their trips accordingly are unlikely to benefit much from widened roads).

Sources:

4. Generally, both in Europe and the U.S.A., 55% of non-recurring congestion is attributed to random incidents and work zones; on German motorways, workzones and crashes account for 60% of congestion causes; in Switzerland, the figures are 33% and 19%, respectively for crashes and work zones. 5. Bovy, P. and Hoogendoorn, S., 2000 and SYTADIN, 2004. 6. American Highway Users Alliance

Page 20 of 123

7. Lee et al (2004), Hwang, K-Y. and Lee, S. (2004) 8. Cairns et al, 2002 9. Chin, S.M. et al (2004) 10. Sylvan, H., Impacts Conference, Stockholm, 29-30 June 2006. 11. Noland and Lem, 2001, Hanley, Dargay and Goodwin, 2002-2003 and Litman, 2005.

2.1.5. Traffic congestion modeling techniques The early forms of traffic congestion modeling relied on using fluid dynamics to analyze traffic streams. Although the propagation of traffic and congestion effects does resemble wave behavior, the causes of congestion are different from the causes of waves in fluids. Therefore, such models are of limited applicability. A good congestion model should be able to factor in people‟s driving decisions on a macroscopic level and live traffic data on a microscopic level.

Analysis of queuing and car-following are two microscopic approaches towards congestion modeling, since queue spillback and driver behavior (such as sudden braking and lane changing) can build up to congestion. However, the existing literature on queuing is based on steady state analysis, which does not represent real traffic flow1.

Sources:

1. Lindsey, C. R., & Verhoef, E. T. (n.d.). CONGESTION MODELLING. Retrieved November 5, 1999

2.1.6. Artificial Intelligence (AI) application in traffic congestion modeling Traffic congestion is known to be caused by qualitative and quantitative factors. Artificial intelligence techniques may be used to correctly determine their impact. The utility of using this method is that AI can be used to not just quantify factors such as human behavior, but can be used to learn patterns (through techniques such as neural networks). This allows any congestion models to factor in new data and unexpected conditions. Fuzzy logic is particularly useful for taking into account the indiscrete nature of qualitative phenomena and allows inputs and outputs to be easily linked through if-then rules.

Studies on traffic congestion have focused on easy-to-capture factors such as vehicle speed and travel time, while leaving out qualitative factors completely or analyzing their impact inaccurately. This is because unless all parameters of these factors are not known, their impacts cannot be fully analyzed by traffic prediction models.

AI is based on mathematical relationships, ensuring a crisp and logical approach towards capturing even the most imprecise and complex phenomena. Numerous real-life applications, ranging from

Page 21 of 123 translating idioms from one language to another to the auto-focus feature on cameras, make use of AI. Applications of AI in transportation include nonlinear prediction, system identification and function approximation, clustering, pattern recognition, optimization and decision making. As a result of the proficiency of these techniques in quantifying qualitative phenomena, we anticipate that they will add a new dimension of accuracy and flexibility to congestion prediction.

2.1.7. Fuzzy Logic Fuzzy Logic is a powerful technique for solving a wide range of industrial control and information processing applications. The fuzzy logic model is empirically based, relying on an operator‟s experience rather than their technical understanding of the system. It handles the concept of partial truth, that is, the truth with values between completely true and completely false. Fuzzy systems take decision on the necessary action based on information from the sensor. Fuzzy logic is flexible and easy to understand as it can model non-linear functions of arbitrary complexity and can be blended with conventional techniques. In fuzzy logic processing involves a domain transformation called fuzzification. Crisp inputs are transformed into fuzzy inputs. To transform crisp inputs into fuzzy inputs, membership functions must be defined for each input. Once a membership functions are defined, fuzzification takes a real time input value such as time and compares it with the stored membership function information to produce fuzzy input values.

Fig. 2.1.7.1: The Fuzzy Logic Process

2.2. Expert Opinions Survey

2.2.1. Questionnaire development An expert opinion form was made in order to prioritize the causes of congestion identified in our literature survey. For each stated factor, a Likert scale of 1 to 5 was provided for quick rating of the factor. It also contained provisions for adding new factors or comments from the interviewees.

Page 22 of 123

2.2.2. Pilot Survey A pilot survey was conducted among various transportation professionals, graduate students and academics. The purpose of this survey was to test the interview form for any flaws in content or design. As a result of this pilot survey, we discovered that an additional factor was required in the form, namely „Whether the road is being used according to its functional classification.‟ The form was modified appropriately, and a final form was made (Appendix A).

Fig. 2.2.2.1: Expert Opinion Form for Causes of Traffic Congestion

Page 23 of 123

2.2.3. Identifying Experts and Conducting Interviews The criterion set for experts was that they must have at least 5 years of experience in the field of transportation and/or have a PhD in a transportation-related area. For conducting the interviews, we visited the Civic Center, where we interviewed several government officers affiliated with mass transit and transport planning. Among our respondents were geometric designers, construction and project managers and design managers. Additionally, some interviewees responded through email.

2.2.4. Factors prioritization Using the relative importance index technique, we prioritized the factors. We found that the factor „Encroachment and poor enforcement‟ was considered by the experts to be the most important form of congestion (Appendix G).

2.3. Arterials Selection for Study and Pilot Survey For our study area, eight arterials of Karachi were selected:

(i) M.A Jinnah Road (ii) Rashid Minhas (iii) University Road (iv) Shahrah-e-Faisal (v) I. I. Chundrigar Road (vi) Shahrah-e-Pakistan (vii) Korangi Road (viii) Karsaz Road (See Appendix C).

Later, while collecting congestion data from Google Maps, some changes were made to this list. Korangi Road was omitted since there was no congestion data available for it. I. I. Chundrigar Road and Karsaz Road were omitted since their length was insufficient for them to be considered major arterials. Shahra-e-Pakistan, Jamshed Road and M. A. Jinnah Road were considered as one contiguous arterial. Sher Shah Suri Road and Nawab Siddique Ali Khan Road were added to this list as one arterial.

We conducted a survey (Appendix B) among transportation officials and traffic police officials for the best selection of arterials for morning peak (7 a.m. to 11 a.m.) & evening peak (4 p.m. to 8 p.m.). Through this survey it was found out that M. A. Jinnah Road is more congested in the morning peak whereas in the evening peak M.A Jinnah Road, Rashid Minhas Road, University Road & Shahrah-e- Faisal are mostly congested.

Page 24 of 123

Fig. 2.3.1: Survey Form for Congestion Levels on Selected Arterials during Morning and Evening Peak

Page 25 of 123

2.3.1. Categorization of Factors The factors influencing traffic congestion are further categorized as static factors & dynamic factors. Static factors are those which do not vary with time, and can be measured through Google Earth. Dynamic factors are time-dependent and are therefore measured on the field.

The list of static and dynamic factors is as follows:

STATIC FACTORS DYNAMIC FACTORS Poor road design (narrow lanes etc.) Travel speed No. of lanes Traffic volume on the road Ease in buying vehicles (car leasing etc.) On-street parking Driving behavior (aggressive, risk-averse Design capacity of road etc.) Pavement condition Poor signal design and synchronization Land use of the area under consideration Heterogeneity of traffic Weather condition Lack of public transport Presence of road intersection at small VIP movement and security checks intervals Bottlenecks (work zones etc.)

Encroachment and poor enforcement Absence/improper implementation of

functional classification of roads

2.3.2. Further Categorization of Factors Some of the causes of congestion chosen after the literature review were re-categorized on the basis of the expert opinion survey. The static factor „bottlenecks‟ was combined with „encroachment‟, as both serve to reduce road capacity. Influencing factors which gave an RII greater than 0.70 are to be selected (Sambasivan, 2007). Therefore, „Poor road design‟ (narrow lanes etc.), „No. of lanes‟, „Weather condition‟, „VIP movement‟ and „security checks‟ are neglected as they are of very low importance; i.e. less than 0.70 according to RII.

Page 26 of 123

S.No. Factors RII Rank 1 Encroachment and poor enforcement 0.99 1 2 Lack of public transport 0.97 2 3 Traffic volume on the road 0.89 3 4 Land use of the area under consideration 0.87 4 5 Pavement condition 0.86 5 6 Ease in buying vehicles (car leasing etc.) 0.81 6 7 Poor signal design and synchronization 0.81 6 8 Driving behavior 0.80 7 Absence/improper implementation of functional 9 0.80 7 classification of roads 10 On-street parking 0.79 8 11 Bottlenecks (work zones etc.) 0.79 8 12 Presence of road intersection at small intervals 0.77 9 13 vehicular mix (too many trucks and cars) 0.77 9 14 Travel speed 0.74 10 15 Design capacity of roads 0.74 10 16 Poor road design (narrow lanes etc.) 0.68 11

17 No. of lanes 0.63 12 18 Weather condition 0.60 13 Neglected

19 VIP movement and security checks 0.60 13

Fig. 2.3.2.1: Factors in Order of Priority

2.3.3. Floating Car Method on University Road For a pilot survey, floating car method was performed on University Road through which we obtained travel time and congestion level i.e. low, medium and high. For this, we recorded a video of the speedometer of the vehicle as we drove through University Road, while noting down congestion level and other data through visual observation at 1 km segments.

Page 27 of 123

2.4. Field Data Collection

2.4.1. Identifying Congestion Hotspots Using Google Maps Identification of the congestion spots on our selected arterials was done with the help of Google Maps. We used the Typical Traffic feature of Google Maps to make a chart of congestion data from 7 a.m. till 10 p.m. (Appendix D).

University Road Direction: Jail Chowrangi to Safoora

Landmark (~ 1 km apart) Time

7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 Jail Chowrangi

Wildlife Aquarium

Babar Hospital (Right)

PIA Garden

Bank Al-Islami (Left) 1 Sir Syed University Lalazar Banquet (Left) Usman Institute of Technology No data available after University Lawn Banquet Hall Light 1 Heavy congestion till Urdu college stop from 4 to 9, thins out steeply onward Saturated Heavy Very Heavy

Fig. 2.4.1.1: Congestion Chart of University Road (From Jail Chowrangi to Safoora)

2.4.2. Consulting Traffic Police After the identification of congestion hotspots using Google Maps, the traffic police was consulted for the verification of these congestion spots. Nearly all the spots were identified correctly according to the contacted officials.

2.4.3. Preparing Pro formas for City-wide Data Collection Different pro formas were prepared for the collection of survey data (Appendix F). These include:

1- Traffic count and driver behavior 2- Pavement condition and encroachment 3- Land use 4- Heterogeneity of traffic

Page 28 of 123

2.4.4. Field Surveys for Identifying Pavement Condition and Encroachment Levels On the selected arterials of Karachi, the congestion spots which were identified were surveyed for the encroachment level & pavement conditions on a scale of 1-5 i.e. 1 means low and 5 means high. The results of this survey can be found in Appendix H.

2.4.5. Survey and Analysis of Pavement Condition Effects on Traffic Congestion A detailed survey of the pavement condition of University Road was conducted. Each direction was divided into 25m segments, which were then assessed for pavement condition. Any distresses on the road were categorized as low, medium or high, and the number of each category of distress was then divided by the surface area of each segment to get the distress density, which was further used to calculate the pavement condition index (PCI). This was followed up with a floating car survey to find out the speeds at different sections of the road, in both directions (Appendix M). The results from this exercise were used in the study “Effect of Pavement Condition on Travel Speed” (See Auxiliary Research Projects).

PAVEMENT CONDITION INDEX (Nipa to Safoora)

S no : Section ID Total deduct value (TDV) q CDV PCI Ranking

2 Section#01 (A-B) 18.08848328 1 18 82 Satisfactory 3 Section#02 (B-C) 22.40802927 3 16 84 Satisfactory 4 Section#03 (C-D) 15.91296225 1 17 83 Satisfactory 5 Section#04 (D-E) 23.03231741 2 16 84 Satisfactory 6 Section#05 (E-F) 32.82086664 2 24 76 Satisfactory 7 Section#06 (F-G) 60.0514045 4 32 68 Fair 8 Section#07 (G-H) 44.83577432 5 24 76 Satisfactory 9 Section#08 HI 63.7231466 3 42 58 Fair 10 Section#09 IJ 70.3081142 4 40 60 Fair 11 Section#10 JK 69.62371307 4 40 60 Fair 12 Section#11 KL 19.18942138 4 10 90 Good 13 Section#12 LM 53.42176247 3 32 68 Fair 14 Section#13 MN 66.32092445 4 38 62 Fair

Fig. 2.4.5.1. Pavement Condition Index of University Road (in 25m sections from Nipa to Safoora)

Page 29 of 123

2.5. Traffic surveillance for capacity assessments at bottlenecks As a pilot study, we recorded videos of traffic at three locations on Rashid Minhas Road. Although this was part of a project to find the capacity at a U-turn, we found the data to be useful for our research on driver behavior and its correlation with traffic heterogeneity.

2.5.1. Data Extraction from Traffic Videos on Rashid Minhas Road

Fig. 2.5.1.1. Rashid Minhas Road and Selected U-Turns

At Rashid Minhas Road, traffic videos were recorded at three locations (from 11:00 a.m. to 10:00 p.m.):

 Pedestrian bridge near Aladin park  Gulshan Chowrangi pedestrian bridge near Fariya Mobile Mall  Pedestrian bridge near Shafique Mor

Page 30 of 123

Fig. 2.5.1.2: Video Recording at Gulshan-e-Iqbal

Fig. 2.5.1.3: Video Recording at Shafique Mor

Data extracted from video at pedestrian bridge near Aladin park

Analysis of driver behavior

Scores were assigned to different lane-changes based on how much they impacted the rest of the traffic platoon. A score of 2 was assigned to a vehicle every time it crossed the lane marker between the fast and center lane. However, if the vehicle crossed the marker between the slow lane and center lane, it was assigned a score of 1, since too few vehicles are usually using the slow lane for the platoon to be disrupted. If it was being driven over a lane marker, it was assigned a score of 1 (detailed in Appendix K)

Page 31 of 123

Raza Raza RM1-00035 RM1-00035 Fast Lane Score Slow Lane Score Car Truck Car Truck Bus Bus (passenger (hiace, (passenger (hiace, Rickshaw/ (minibus, Raw Rickshaw/ (minibus, Raw Minutes car, hi-roof, Bike hilux, Minutes car, hi-roof, Bike hilux, qingqi large Score qingqi large Score Suzuki pick- larger Suzuki pick- larger bus) bus) up), trucks) up), trucks) 0-5 34 58 21 7 0 120 0-5 7 38 17 1 3 66 5 to 10 44 71 17 4 0 136 5 to 10 12 51 18 1 2 84 10 to 15 41 71 27 4 4 147 10 to 15 4 25 15 0 3 47 15 to 20 45 60 25 4 1 135 15 to 20 8 42 17 0 3 70 20 to 24:36 29 58 11 8 2 108 20 to 24:36 5 21 16 2 6 50

Raza Fig. 2.5.1.4. Computing a score for vehicle-specificRaza driver behavior for the fastTruck and stopped for first 1:45 RM1-00036 slow lanes of one direction of Rashid MinhasRM1-00036 Road (near Aladin Park) Fast Lane Score Slow Lane Score

Car Truck Car Truck Bus Bus Traffic(passenger counts (hiace, (passenger (hiace, Rickshaw/ (minibus, Raw Rickshaw/ (minibus, Raw Minutes car, hi-roof, Bike hilux, Minutes car, hi-roof, Bike hilux, qingqi large Score qingqi large Score TrafficSuzuki pick-count was observelargerd for different types of modes i.e.Suzuki bus, pick- trucks, cars, larger bus) bus) up), trucks) up), trucks) motorbikes at five-minute interval at the three different locations on Rashid Minhas 0-5 33 74 11 10 1 129 0-5 5 35 28 2 2 72 5 to 10Road 41(detailed75 in Appendix13 6 L). 7 142 5 to 10 6 39 19 3 2 69 10 to 15 39 80 18 4 3 144 10 to 15 10 36 16 0 1 63 15 to 17:47 17 41 8 2 2 70 15 to 17:47 2 34 17 1 0 54 TIMES CARS BUSES/TRUCKS BIKES RICKSHAW/QINCHI 11:30 0 0 0 0 Raza 11:35 116 16 194 Raza 54 380

RM1-00037 11:40 91 19 168 RM1-00037 41 319 11:45Fast Lane Score115 18 163 50 Slow Lane Score 346 11:50 79 13 179 77 348 Car Truck Car Truck Bus Bus (passenger 11:55 95(hiace, 20 175 (passenger67 357(hiace, Rickshaw/ (minibus, Raw Rickshaw/ (minibus, Raw Minutes car, hi-roof, Bike12:00 98hilux, 22 183 Minutes car, hi-roof,63 Bike 366hilux, qingqi large Score qingqi large Score Suzuki pick- 12:05 104larger 21 176 Suzuki pick-57 358larger bus) bus) up), 12:10 124trucks) 12 167 up),57 360trucks)

0-5 32 7212:15 13 96 1 7 14 125 202 0-5 3 48 30 12 360 45 5 to 10 39 5612:20 8 100 4 3 15 110 189 5 to 10 9 60 25 14 364 39 10 to 15 39 7912:25 14 106 3 4 14 139 178 10 to 15 4 49 34 10 347 48 15 to 20 49 7112:30 11 109 3 1 30 135 192 15 to 20 6 49 32 8 380 46 20 to 25 38 72 10 8 1 129 20 to 25 11 55 18 84 12:35 103 16 209 54 382 25 to 30:25 49 90 12 6 1 158 25 to 30:25 18 45 15 78 12:40 100 22 230 40 392 12:45 95 11 232 54 392 12:50 110 16 262 52 440

Fig. 2.5.1.5: Time-based Traffic Volumes for Shafique Mor

This data was used to compute various parameters and trends such as mode-based traffic flow, flow variation and operational capacity for each location.

Page 32 of 123

Fig. 2.5.1.6: Operational Capacity

Fig. 2.5.1.7: Flow Variation at Gulshan-e-Iqbal

Fig. 2.5.1.8: Mode-wise volume at Shafique Mor

Page 33 of 123

2.6. Tasks in progress

2.6.1. Fuzzy Logic Model Definition

Fuzzy logic is a form of multi-valued logic derived from a fuzzy set theory that deals with reasoning that is approximate rather than precise. Fuzzy logic is a superset of the Boolean-conventional logic that has been modified to comprehend the conception of partial truth and truth values between completely true and complete false. Fuzzy modeling develops a possibility to translate statements into natural language. The functioning is based on mathematical tools. The basic operations of the set theory are intersection AND, union OR, and complement NOT extended for the purpose of fuzzy logic.

Fuzzy Expert System

Fuzzy expert systems are based on fuzzy if-then rules that relate one input variable with other output variable which are in the form of linguistic values. The if-then rules are composed of fuzzy antecedents or premises represented by the membership functions of the input variables and fuzzy consequents or conclusions represented by the membership functions of the output variables. An example of a fuzzy expert rule is “If the crew skill level is low and the crew ratio of apprentices to journeymen is large, then the productivity is low.”

Membership Functions used in Fuzzy Expert Systems

The membership function is a graphical representation of the degree of involvement of each input variable. It comprises weight which is analyzed through the overlapping of the functions of input variables to give an output variable. There are different types of membership functions; the most common includes the triangular, trapezoidal, Gaussian and generalized bell shaped.

Triangular

This membership function uses three parameters a, b and c, as shown in Fig. 2.6.1.1. Through the combination of the min-max expressions, the coordinates of the x-axis are calculated.

Page 34 of 123

Trapezoidal

Four parameters are used by the trapezoidal membership function such as a, b, c and d as shown in Fig. 2.6.1.1. Min-max expressions determine the x- coordinates of the trapezoidal membership function.

Gaussian

Two parameters have been used in the Gaussian membership function such as c and σ as shown in Fig. 2.6.1.1. The parameter c is the centre of the membership function and σ represents the width of membership function and is used to calculate the Gaussian membership function.

Generalized Bell

It has three parameters a, b and c as shown in Fig. 2.6.1.1. The generalized bell membership function is calculated by using a, b and c which represent the length, height and centre of the membership function respectively.

Fig. 2.6.1.1: Types of Membership Functions

Page 35 of 123

Fuzzy Inference System

The Fuzzy Inference System is a popular computing framework based on the concept of the fuzzy set, fuzzy theory, fuzzy if-then rule and fuzzy reasoning. The basic structure of the Fuzzy Inference System consists of three components: rule base, which contains the selection of rules: database, which defines the membership function used in the fuzzy rules and the reasoning mechanism; which performs the inference procedure. There are three different types of the Fuzzy Inference Systems which are different from each other on the basis of their different consequent of the rules and the different Defuzzification methods.

Mamdani Fuzzy Inference System

The Mamdani Inference System was first proposed in 1975 by Mamdani and Allisian. The mechanism of the Mamdani Inference System is explained in detail in the next section similarly as mentioned by Negnevitsky.

Mechanism of Mamdani Fuzzy Inference System

The Fuzzy Inference System is divided into four phases: fuzzification, rule evaluation, rule aggregation and defuzzification. For illustration it is assumed that two inputs, project funding (x), and project complexity (y) are required to estimate the output which is project performance (z). In this example “x”, “y” and “z” are linguistic variables and “A1”, “A2” and “A3” (inadequate, marginal and adequate) are the linguistic values of the universe of discourse “X” that is project funding. In the same way, B1 and B2 (high and low) are the linguistic values for the input project complexity at the universe of discourse of “Y.” The linguistic values for the output variable project performance are C1, C2 and C3 (low, average and high) at the universe of discourse of “Z”. Three rules have been determined through experience which includes:

Rule 1: if x is A3 OR y is B1 then z is C1

Rule 2: if x is A2 AND y is B2 then z is C2

Rule 3: if x is A1 then z is C3

Page 36 of 123

Sugeno Fuzzy Inference system

The Sugeno Fuzzy inference system also known as TSK (Takagi, Sugeno and Kang) developed in 1985 is similar to the Mamdani Inference System. Among the four components of the Fuzzy Inference Systems, the first three components performed similar to the Mamdani Inference System. However, in the Sugeno Inference System, the output membership function can be linear or constant. The consequent output of each rule is weighted with the firing strength of the rule using the AND operator. The output has been calculated through the weighted average of all the rule outputs which can be calculated by using the equation (23).

Tsukamoto Fuzzy Inference System

The system in which the consequent of each fuzzy if-then-rule is represented by a fuzzy set with a monotonical membership function is described as the Tsukamoto Fuzzy Inference System. The firing strength of the rule helps in calculating the crisp value of the output of each rule.

De-fuzzification Methods used in Fuzzy Inference System

De-fuzzification is used to transform the fuzzified output values into crisp values or into numbers. There are different De-fuzzification methods used in Fuzzy Inference Systems; however, the most commonly used are Mean of Maximum (MOM), Centre of Gravity (COG), Largest of Maximum, (LOM), Sum of Maximum (SOM) and Bisector, weighted average, weighted sum etc.

Mean of Maximum (MOM)

This method is used in the Mamdani Inference System. In this method the mean is taken for the maximum values of the output of the membership functions for converting the fuzzified output into crisp output. However, this method is suitable to be used when there are peaked values of output. The graphical representation of the MOM method is shown in Fig. 2.6.1.2.

Page 37 of 123

Fig. 2.6.1.2: Mean of Maximum (MOM)

In Fig. 2.6.1.2, µ represents the membership function and z is the fuzzified values of the output variable.

Centre of Gravity (COG)

This is the most widely used method for converting fuzzy output into De-fuzzified output or crisp output and is mostly used in the Mamdani Inference System This method becomes complicated in the case of complex types of membership functions. In this method, the centre of gravity or the centre of area has been measured for calculating the crisp output. COG is represented by Fig. 2.6.1.3.

Fig. 2.6.1.3: Centre of Gravity (COG)

Fig. 2.6.1.3 shows µ as the membership function z* is the crisp output and z is the fuzzified values of the output variable.

Page 38 of 123

Last of Maximum (LOM)

In this method, the last value of the maximum values of the membership functions of the output has been selected to be converted into a crisp output. The Mamdani Inference System uses this method of defuzzification.

Fig. 2.6.1.4: Last of Maximum (LOM)

Fig. 2.6.1.4 shows µ as the membership function z1* is the second to last of the maximum membership functions, z2* is the last of membership functions and z is the fuzzified values of the output variable.

Smallest of Maximum (SOM)

This method converts the smallest value of the maximum values of the membership function of the output into crisp output. It is used in the Mamdani Inference System.

Fig. 2.6.1.5: Smallest of Maximum (SOM)

In Fig. 2.6.1.5, µ represents the membership function and z is the fuzzified values of the output variable.

Page 39 of 123

Bisector Method

In this method two lines bisect through a vertical line and divide into two regions. The vertical line may pass through the centre of the region. Fig. 2.6.1.6 shows the graphical representation of the bisector method. This method is also used in the Mamdani Inference System.

Fig. 2.6.1.6: Bisector Method

Weighted Average Method

This method is used in the Sugeno Inference System. In this method, the average of the weights of the values of the membership function of the output received at each rule has been taken. This method provides precise results and it is simpler and computationally faster.

Fig. 2.6.1.7: Weighted Average Method

Fig. 2.6.1.7 represents µ as the membership function z* is the crisp output, z is the fuzzified values of the output variable, a, b, c are the weighted averages of the values of the membership functions of output.

Page 40 of 123

Weighted Sum Method

The summation of the weights of the values of the membership function of the output received at each rule has been calculated in this method in order to calculate the crisp output. It is also used by the Sugeno Inference System This method has been used to reduce the computational burden of the weighted average method however, it may cause the inefficiency of the linguistic accuracies of the output.

Fig. 2.6.1.8: Weighted Average Method

Fig. 2.6.1.8 represents µ as the membership function z* is the crisp output, and z is the fuzzified values of the output variable.

Development of Fuzzy Logic (FL)

Fuzzy Logic models have been developed using the Fuzzy logic toolbox in MATLAB version 7.8.34 (2009a). The most common parameters of the models include the shape of the membership function, number of the membership function, type of inference system, type of defuzzification method, type of fuzzy operators etc.

Page 41 of 123

Fig. 2.6.1.9: Fuzzy Inference System

Development of Membership Functions

There are 15 input variables and 1 output variable which are represented by fuzzy set F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, F11, F12, F13, F14, F15 and TS. The input and output variables consist of six dynamic including output variable and nine static variables. The triangular M.F has been used for all the input and output variables with three linguistic terms. The Likert scale of 1 to 5 for each input variable and output variable of the fuzzy set has been distributed into five linguistic terms. The Fuzzy Logic Tool Box in MATLAB version 7.8.3 (2011a) was used to develop the membership function as shown in Fig. 2.6.1.10. However, the Fuzzy Logic prediction model will be executed through a code in order to verify the results of the Fuzzy Logic Tool Box.

Page 42 of 123

Fig. 2.6.1.10: Fuzzy Logic Toolbox; Membership Functions

Development of Fuzzy Rules

Fuzzy rules will be developed based on the Fuzzy Logic prediction model of fifteen influencing factors (F1 to F15) and Travel speed (TS) separately. The equation (1) shown below was used in the literature for developing fuzzy rule is equal to:

The Fuzzy Inference System (FIS) was used in the Graphical User Interface (GUI) representing the fuzzy rules and the Rule Viewer has been shown in Fig. 2.6.1.11.

Page 43 of 123

Fig. 2.6.1.11: Graphical User Interface (GUI) for Fuzzy Rules

In this study, there are fifteen numbers of input variables and three numbers of membership functions. According to the above formula, the numbers of rules required are seventeen millions. This formula is not feasible in this study due to the impracticality of developing an exponential number of rules. Therefore, the Correlation Coefficient analysis will be carried out. Since the data is non-parametric therefore, Spearman‟s rank Correlation Coefficient will be conducted.

2.6.2 Data Preparation for Fuzzy Logic Model As discussed in the previous chapter eight Arterials of Karachi were identified. Those arterials were coded as:

(ix) A1= M.A Jinnah Road (x) A2= Rashid Minhas (xi) A3= University Road (xii) A4= Shahrah-e-Faisal (xiii) A5= I.I. Chundrigar Road

Page 44 of 123

(xiv) A6= Shahrah-e-Pakistan (xv) A7= Korangi Road (xvi) A8= Karsaz Road. These Arterials were further divided into different segments. The length of each segment is equal to 200 ft. The segments were coded as:

S11= First segment for Arterial A1

S12= Second segment of Arterial A1

S21= First segment of Arterial A2 and so on.

Traffic congestion in terms Travel speed is observed against fifteen influencing factors. Travel Speed is coded as TS. The coding of the influencing factors is shown below in Table 2.6.2.1.

Page 45 of 123

Code Influencing Factors

F1 Encroachment and poor enforcement

F2 Lack of public transport

F3 Traffic volume on the road

F4 Land use of the area under consideration

F5 Pavement condition

F6 Ease in buying vehicles (car leasing etc.)

F7 Poor signal design and synchronization

F8 Driving behavior

Absence/improper implementation of F9 functional classification of roads

F10 On-street parking

F11 Bottlenecks (work zones etc.)

Presence of road intersection at small F12 intervals

F13 vehicular mix (too many trucks and cars)

F14 Poor road design (narrow lanes etc.)

F15 No. of lanes

TS Travel Speed

Table 2.6.2.1: Influencing Factors Coding

Page 46 of 123

A data collection form was prepared that shows the arterials, intervals number, interval duration, segments, fifteen influencing factors and Travel speed as indicated in the Table 2.6.2.2 given below.

Table 2.6.2.2: Data Collection Form

Page 47 of 123

The unit of measurement of these influencing factors and travel speed is also analyzed and identified as shown in Table 2.6.2.3:

Unit Description

Encroachment and 1= Minimum lane occupied, static F1 Scale 1 to 5 poor enforcement 5= Maximum Lane Occupied

Lack of public 1=maximum number of buses , static F2 Scale 1 to 5 transport 5= minimum number of buses

Traffic volume on No of vehicles dynamic F3 the road passed

1= Residential, 2= Residential Land use of the + commercial, 3= static F4 area under Scale 1 to 5 Recreational, 4= Educational, consideration 5= Commercial

Pavement 1= Excellent condition, 5= static F5 Scale 1 to 5 condition worst Condition

Ease in buying static F6 vehicles (car Scale 1 to 5 1= most difficult, 5= most easy leasing etc.)

Poor signal design 1= good design, 5= Poor static F7 and Scale 1 to 5 Design synchronization

% of vehicles vehicle change/traffic count * dynamic F8 Driving behavior change lane 100

Absence/improper implementation of 1= proper classification, 5= static F9 functional Scale 1 to 5 improper classification classification of road

Page 48 of 123

No. of Vehicles dynamic F10 On-street parking parked

Bottlenecks (work 1= minimum lane width drops, static F11 Scale 1 to 5 zones etc.) 5= maximum lane width drop

Presence of road 1= small no. of intersection, 5= static F12 intersection at Scale 1 to 5 large number of intersection small intervals

vehicular mix (too dynamic F13 many trucks and % of T/B (5-30%) cars)

Poor road design 1= good design, 5= Poor static F14 Scale 1 to 5 (narrow lanes etc.) Design

static F15 No. of lanes number of lanes

200m distance, 10 sec= dynamic TS Travel Speed Km/hr 100km/hr

Table 2.6.2.3: Unit of Measurement of Influencing Factors

Page 49 of 123

Traffic surveillance data which was recorded at Rashid Minhas road was observed and calculated according to the unit of measurements described in Table 2.6.2.4.

Table 2.6.2.4: Data collection form

The values of the influencing factors and travel speed have different ranges therefore they are required to be normalized between 0 and 1.

Data Normalization

In order to incorporate the variance in between the values of the influencing factors and travel speed, the data will be required to normalize in the range of 0 to 1 by using the formula as shown in equation (1):

……. (1)

Where Xn was the normalized value, Xp was the respective value in the data sample, Xmin was the minimum value of the data sample and Xmax was the maximum value of the data sample.

The normalized values are shown in Table 2.6.2.5.

Page 50 of 123

Table 2.6.2.5: Normalized Values

2.6.3. Field Surveys of Congestion Hotspots The next task is to record traffic videos at the congestion hotspots identified on the arterials in Appendix E.

2.7. Further tasks The remaining tasks (in order of completion) include

1. Investigating how driver behavior can be „measured‟ so that it can be input in our model 2. Determining how „Ease in buying vehicles‟ can be quantified 3. Conducting surveys to determine whether signal synchronization issues are affecting the congestion hotspots 4. Similar quantification and measurement of remaining factors 5. Model development and calibration 6. Testing the model

2.8. Fund utilization The duration of the project is of two years. HEC has allocated a total of Rs. 3,703,000 for this project, out of which Rs. 2,139,000 is already received as Year 1 layout, and being utilized

Page 51 of 123 within their respective heads, while Rs. 1,564,000 is to be disbursed in the second year of research.

A separate account is being maintained by DF-NEDUET and all disbursements are carried out with the approvals of VC under advice from Resident Auditor, NEDUET. This channel ensures all fund utilization to be within HEC earmarked heads as well as following SPPRA rules and regulations. The major heads of fund utilization are as follows:

2.8.1. Research staff Dedicated research staff has been appointed within the budget allocated, in order to facilitate smooth running of the project.

Designation Name Qualification

Research Assistant S. M. Raza Jafri B.E. (Urban Engineering, NEDUET)

Research Support Staff Taimoor Hassan Babar B.S. (Civil Engineering, BUITEMS)

Research Support Staff Aakefa Qaiser B.E. (Urban Engineering, NEDUET)

2.8.2 Equipment

Proposed Equipment Purpose Equipment Procurement Unit Budget (in PKR) Links for further information

Video Recording Traffic Volume Counts, 100,000 (50,000 High-definition camera and mount (Price = System (2 Cameras + since DVR is included in 1 for camera, approximately Rs. 50,000/-) 1 DVR) item below 50,000 for DVR)

Car black box with GPS and DVR. This device can record http://www.alibaba.com/product- Electronic Distance Recording Location the vehicle speed and station (location) along with its 190,000 (95,000 detail/4ch-black-box-3g-car- 1 Measuring Tool (2) Parameters function as a DVR. Requires a power supply (Price = each) dvr_692629041.html?spm=a2700.7724 approximately Rs. 8,000/-) 857.0.0.htg6pO

Personal trackers, since they can transmit location, Short Distance distance and speed data much more conveniently. They Measurements http://trackimo.com/ 0315-3671360 Laser Gun can also be used by several people simultaneously, and 5 200,000 (headways, distance 0322-2407068 in vehicles that lack a power supply. (Price = covered by vehicle etc.) approximately Rs. 70,000/-)

2.8.3 Expendable supplies 1. Field work expenses 2. Data Extraction 3. Journal Publication Fee 4. Stationery/Contingency 5. Communication

Page 52 of 123

6. Institutional Overhead 7. Local Travel 8. Miscellaneous

2.8.4 Publications Under the procurement head of “publications”, international state-of-the-art books are procured. Details are given below:

S. No. Title / Edn. / Vol. / Year Author Publisher ISBN/ ISSN Price (Rs.)

(Please Use Capital Letters) (1st & Last Name)

Shigeru Morichi, Surya Raj Springer Berlin 1 TRANSPORT DEVELOPMENT IN ASIAN MEGACITIES 978-3-642-29742-7 6,128 Acharya Heidelberg

ISBN-10:

TRANSPORTATION SYSTEMS ANALYSIS: MODELS AND 387758569 Springer; 2nd Edition 2 APPLICATIONS (SPRINGER OPTIMIZATION AND ITS Ennio Cascetta 16,000 (September 15, 2009) APPLICATIONS)/2ND EDITION ISBN-13:

978-0387758565

ISBN-10: 0415285151 THE ECONOMICS OF URBAN TRANSPORTATION/2ND Kenneth Small, Erik Routledge; 2nd Edition 3 6,128 EDITION Verhoef (November 15, 2007) ISBN-13: 978- 0415285155

SPATIAL ANALYSIS METHODS OF ROAD TRAFFIC Becky P. Y. Loo, Tessa 4 CRC Press 978-3-642-29742-7 12,000 COLLISIONS Kate Anderson

INTRODUCTION TO INTELLIGENT SYSTEMS IN TRAFFIC ISBN-13: 978-1627052078 AND TRANSPORTATION (SYNTHESIS LECTURES ON Ana L. C. Bazzan, Morgan and Claypool 5 4,000 ARTIFICIAL INTELLIGENCE AND MACHINE Franziska Klügl Publishers LEARNING) 1ST EDITION ISBN-10: 1627052070

ISBN-10: 9067641715 ARTIFICIAL INTELLIGENCE APPLICATIONS TO TRAFFIC Bielli (Editor), Ambrosino (E 6 CRC Press 6,000 ENGINEERING 1ST EDITION ditor), Boero (Editor) ISBN-13: 978-9067641715

ECONOMICS OF URBAN HIGHWAY CONGESTION AND McDonald, J. F., D'ouville, 7 Springer ISBN 978-1-4615-5231-4 17,500 PRICING Edmond L., Louie Nan Liu

Authors: John C. ISBN: 978-3-319-15164-9 ROAD TRAFFIC CONGESTION: A CONCISE GUIDE, 8 Falcocchio, Herbert S. Springer (Print) 978-3-319-15165-6 11,656 VOLUME 7 2015 Levinson (Online)

Page 53 of 123

SECTION 3. AUXILIARY RESEARCH PROJECTS

This project is running concurrently with a few other research projects, allowing us to share data and streamline our efforts.

3.1. Correlation between Driver Behavior and Traffic Heterogeneity We see whether certain types of driver behavior (such as lane changing and sudden braking) are affected by the traffic heterogeneity (a measure of how diverse the vehicles are in the traffic stream for a given time period).

Overview

Heterogeneity of traffic is known to affect various traffic parameters such as speed, headway and flow. Intuition suggests that the heterogeneity may also affect driver behavior, similar to the findings of „shared space‟ experiments. These experiments found that confusing the drivers by removing road signage and demarcation structures caused them to slow down, resulting in improved safety. By corollary, driver behavior may be more sensitive to a diverse mix of vehicles as opposed to a homogenous, „expected‟ mix. In a heterogeneous mix of traffic, drivers may be unsure of how much headway to maintain with respect to the different vehicles, and more overtaking or lane changing may occur due to the differences in speeds and accelerations between the different vehicles. The resulting visual distractions (due to unexpected vehicle types appearing in the driver‟s field of vision) and cognitive distractions (from thinking about how much headway to maintain) are two of the four kinds of distractions known to affect drivers (Stutts et al., 2005).

As the diversity of the mix increases, a sense of inequality may arise in some road users, leading to a competition often based on the size/quality of the competitors‟ vehicles (Novaco, 1989). Road rage, excessive signaling or honking, and overly aggressive or conservative driving may therefore be some of the associated effects of traffic heterogeneity.

Of course, driver behavior is also influenced by factors like traffic volume, pavement condition and local knowledge of roads (for example, slowing down before reaching an area notorious for its jaywalkers). Any attempts to correlate driving behavior and traffic heterogeneity should consider locations and timings where the other factors are minimized. A stretch of road with satisfactory pavement condition, fairly uniform traffic volumes (with few breakdowns in flow) and no aberrant conditions (such as wrong-way movement, jaywalking and encroachment) will yield ideal data for this correlation.

Page 54 of 123

Prior Considerations

In Karachi, two aspects must be considered before any analysis on driver behavior or traffic flow. Lane-changing is very common, primarily due to the proliferation of motorcycles (and the ease with which they can be maneuvered through heavy traffic) and the absence of a bus lane (or enforcement of one). Roads are also irregular in width (the number of lanes changes frequently along their length), and are often encroached upon. Adherence to a single lane is therefore highly short-lived, and is often forgone in favor of faster driving. Secondly, traffic is highly heterogeneous. Due to poor enforcement of vehicle standards and fitness, all manner of trucks, retrofitted buses, carts and non-standard vehicles such as Qingqis (and even some non-vehicles) may be seen plying Karachi‟s major roads.

Due to such frequent lane-changing, vehicle speeds in Karachi have been observed to be minimally affected by this ostensibly chaotic behavior. In particular, motorcyclists are commonly seen weaving through traffic with almost no effect on adjacent vehicles. This may be attributed to not just their speed, maneuverability and small size, but also to conflict psychology with regard to motorcycle collisions. With little to no insured vehicles on Pakistan‟s roads, vehicular damages suffered in collisions often result in on-the-spot payments made after negotiations between the affected parties. Regardless of who is actually at fault, the motorcyclist is rarely in a better position than the owner of the other, usually larger, vehicle during the negotiations. Even though the motorcyclist is likely to suffer far worse injuries in a collision, they are also more likely to cause more damage to the larger vehicle (in monetary terms), and be on the wrong side of the law (since most accidents occur due to motorcyclists weaving through traffic). It may therefore be said that they are „expected‟ to bear the costs in event of a collision, making owners of other vehicles less concerned about avoiding a collision with them.

Motorists have also adapted to the erratic weaving, stopping and merging patterns of buses and the notorious driving methods of truckers, opting to maintain an ample distance from these vehicles rather than vie for road space. Pedestrians running across high-speed traffic are not an uncommon sight on Karachi‟s arterials. The cumulative effect of exposure to such anomalous driving conditions may serve to temper the effect of traffic heterogeneity on driver behavior in Karachi.

Page 55 of 123

Fig. 3.1.1: Traffic Heterogeneity vs. Driver Behavior

As this is an ongoing project, more information will be available as the research continues. The project is expected to be completed by September 2016.

3.2. Effect of pavement conditions on travel speed Correlations between pavement defects of different types and severity and vehicular speed are determined.

Introduction

When the road is first built it is typically in good condition. With the passage of time and with the continuous application of traffic loads the pavement gradually deteriorates and the condition gets worse. Traffic performance is affected by many factors and can easily be predicted. Traffic characteristics that affect the performance are traffic load, traffic volume, tyre pressure and vehicle speed. This paper deals mainly with pavement condition effects on vehicle speed. In the planning and design process for all aspect of road network, traffic flow parameters estimation is crucial as such travel time, which is the reciprocal of speed. The influence of pavement surface conditions on travel time has been under-reported and obviously drivers may choose to drive more slowly over a surface that has deteriorated than they would driver over a more even surface. Adverse conditions such as traffic congestion, inclement weather and pavement distress among others have significant impacts on vehicle

Page 56 of 123 speed and traffic flow. Based on the study carried out by Akinmade Oluwatosin Daniel, Danladi Slim Matawal, Francis Aitsebaomo and Emeso. B. Ojo in 2014, they gathered the vehicle speed data in Nigeria and concluded that significant reduction in travel time by more than 50% and significant reduction in traffic flow by up to 30% to 40% would result from adverse road surface condition[1].

Result and Discussions

This study is based on the fact that significant vehicle speed loss would result from pavement distresses. The aim behind this exercise is to establish the effect of pavement condition. For the purpose of estimating traffic performance the relationship between Speed and PCI values in a situation of free flow was used. Within the preview of study objectives, we set out road sections with different kind of distresses. The sections are surveyed and the empirical result is investigated.

In light of evidences obtained from the examination of survey data, the analytical findings of road sections were considered. The empirical results from surveyed sites showed that the section having more distresses and having lower PCI values have a low average speed. Some observations are outliers because that indicates an indirect relation between PCI values and speed which may be possible in real time. People may change their direction when there is distress in pavement, and speed does not change. These outliers are not considered in final result.

The trend of graph is increasing having positive slope indicating the direct relationship between PCI and speed of the vehicle.

Page 57 of 123

Fig. 3.2.1: Regression Model of Pavement Condition Index vs. Speed

Based on the findings of the study, it can be concluded that:

• Adverse conditions in pavement have significant impact on the traffic performance.

• There is a significance change in vehicle speed with the pavement distress sections.

• There is direct relationship between the PCI (Pavement Condition Index) and speed of the vehicles.

References

1. Akinmade Oluwatosin Daniel, Danladi Slim Matawal, Francis Aitsebaomo and Emeso. B. Ojo (OCTOBER 2014). The Extent of Travel Time Increment due to Pavement Distress, ARPN Journal of Engineering and Applied Sciences.

3.3. Capacity of U-Turn near Aladdin Park (FYP) U-turns are used to facilitate the traffic in urban arterials in developing countries. They manoeuvre the traffic into the opposite direction by making them turn about 180 degrees. Large metropolitan cities use U-turns as a diverging movement and that has impact on the

Page 58 of 123 through traffic in that it interrupts the through traffic movement. There are a number of factors that may be concerned for capacity analysis of U-turns at signal free corridors as such its effect on the capacity of road, as the U-turn vehicles wait for a large enough gap before making the manoeuvre. There are interactions between through traffic and U-turns traffic streams. When the through traffic volume increases, it lessens the chances for the U-turns traffic to move. This is of major concern that whether it is useful to allow U-turns to be made in future considering the current situation at signal free corridors, or it is better to use of signalized intersection. The main focus of this report is to analyse the capacity of the traffic flow that uses U-turns and investigate whether it is a convenient method of using u turns or should there be alternatives to be used in the future to solve the problems of traffic congestion in metropolitan cities such as Karachi. Apart from using U-turns there is another alternative that is used in Karachi as well as other big cities around the world: making a signalized intersection where traffic has to wait for designed time period at signal that also has impact on the capacity of the road.

Objectives

The major objective of the project is to form a probabilistic methodology to analysis the conflict points, traffic jams and traffic congestion due to U-turns at Signal Free Corridor.

• Capacity Analysis of U-turns at Signal Free Corridor.

• Proposal of Signalize intersection.

• Comparison of proposed signalizes intersection with existing U-turns.

Scope & Limitation

This project has a vast scope in solving our current situation of the traffic congestion due to U-turns at signal free corridors and in establishing a research that is focused on the operational performance of U-turning to straight movement. The operational effects of U- turning heavy vehicles would not be considered. This research analysis the different possible outcomes of using U-turns that affects the road capacity, and to provide a suitable possible substitute that can increase the road capacity in negligence to traffic jams and congestion in the roads and to provide a sustainable transportation environment in urban arterials. In addition, this research is limited to the urban and suburban environments.

Page 59 of 123

Fig. 3.3.1: Number plate data for finding travel times of vehicles between locations

Page 60 of 123

SECTION 4. APPENDICES

Appendix A: Expert Opinion Form for Causes of Traffic Congestion 65

Appendix B: Survey Form for Congestion on Arterials 69

Appendix C: Map of Selected Arterials of Karachi 73

Appendix D: Congestion Chart 76

Appendix E: Plan for Recording Traffic Videos at Selected Locations and Times 82

Appendix F: Pro formas 84

Appendix G: Relative Importance Index for Prioritizing Factors 87

Appendix H: Encroachment and Pavement Condition Data at Selected Locations 89

Appendix I: Number and Width of Lanes of Selected Roads (Static Factors) 95

Appendix J: Land Use (Static Factors) 105

Appendix K: Driver Behavior (Dynamic Factors) 111

Appendix L: Traffic Counts 113

Appendix M: Speed Observations for University Road 116

Appendix N: Financial Statement 119

Page 61 of 123

Appendix A: Expert Opinion Form for Causes of Traffic Congestion

Page 62 of 123

Page 63 of 123

Page 64 of 123

Page 65 of 123

Appendix B: Survey Form for Congestion on Arterials

Page 66 of 123

Page 67 of 123

Page 68 of 123

Page 69 of 123

Appendix C: Map of Selected Arterials of Karachi

Page 70 of 123

Page 71 of 123

Key: White – Korangi Road

Green – Shahra-e-Faisal

Pink – I. I. Chundrigar Road

Yellow – M. A. Jinnah Road

Orange – Karsaz Road

Red – University Road

Cyan – Shahra-e-Pakistan

Blue – Sher Shah Suri Road

Black – Rashid Minhas Road

Note: Karsaz Road, I. I. Chundrigar Road and Korangi Road were omitted from our study due to lack of congestion data on Google Maps or insufficient arterial length. M. A. Jinnah, Jamshed Road and Shahra-e-Pakistan were considered as one contiguous arterial.

Similarly, Sher Shah Suri Road and Nawab Siddique Ali Khan Road were studied as one arterial.

Page 72 of 123

Appendix D: Congestion Chart

Page 73 of 123

Page 74 of 123

Page 75 of 123

Page 76 of 123

Page 77 of 123

Page 78 of 123

Appendix E: Plan for Recording Traffic Videos at Selected Locations and Times

Page 79 of 123

Page 80 of 123

Appendix F: Pro formas

Page 81 of 123

TRAFFIC COUNT SURVEY LOCATION: SECTION (TO/FROM): DATE: STATION NO. : DIRECTION: DAY: ROAD NAME:

VEHICLES TIME(min) BUS TRUCK CAR MOTORCYCLE/RICKSHAWS TOTAL

00:00-10:00 00:10-00:20 00:20-00:30 00:30-00:40

Page 82 of 123

Page 83 of 123

Appendix G: Relative Importance Index for Prioritizing Factors

Page 84 of 123

Page 85 of 123

Appendix H: Encroachment and Pavement Condition Data at Selected Locations

Locations (200m apart) Pavement Condition Encroachment Comments

Jail Chowrangi to Bank Al-Islami

Jail Chowrangi 1 1 First Chowki 1 3 Cross Road at end of Jail 2 5 Cross road after Pedestrian crossing 2 5 Just after U-Turn 2 5 Wildlife Aquarium (Just after entrance) 2 5 Large billboard outside Askari Park 1 5 Askari Park end gate 1 4 Algaso Fuel Station (Right) 1 3 Shell Petrol Pump 2 3 Babar Hospital (Right) 1 2 Jaama Masjid Mujaddid Sani 1 2 Off ramp near Civic Center 3 2 Road junction before pedestrian bridge 2 1 Billboard in front of expo center 3 1 Innovative IT Training institute 4 2 Just before Pedestrian Crossing 3 1 Pizza Crust 3 4 Bank Islami (Right) 4 2 Just before Al Mustafa Medical Center 4 3 Bank Islami

Page 86 of 123

Safoora to Jail Chowrangi Sir Syed University (Right) 1 1 Saleem Center (Right) 2 2 Opposite cricket ground (Left) 3 1 Bank Al Habib (Right) 4 2 Jofa Towers (Right) 3 1 Sindh Bank (Right) 3 1 Soneri Bank (Right) 2 2 Technomen (Right) 2 1 Bank Islami (Left) 2 1 Pizza Crust (Right) 1 2 Just after pedestrian crossing 1 2 Innovative IT Training Institute 2 2 Expo Center Gate 1 1 Junction after Pedestrian Bridge 1 1 Off Ramp near Civic Center 2 3 parking Jama Masjid Mujaddid Sani 2 5 Babar Hospital

Page 87 of 123

Locations (200m apart) Pavement Conditions Encroachment Comments

C.O.D. Flyover to Nagan Chowrangi Lavish Dine 2 1 Traffic Island 1 3 Just before Magna Mall 1 3 Just after Honda Showroom 1 4 4 Seasons Banquet

Cross Sohrab Goth

Hashim Khan Quetta Hotel 2 1 100m after CNG Station on right 1 2 100m after Café Allah o Akbar 1 1

Intersection with R.A. Jafri Road (Dayyar e Shereen)

Keep Going 1 1 BBQ and Roll Point (Left) 1 1 PSO Pump (Left) 1 1 Family Park (Right) 1 2 Nagan Chowrangi

Page 88 of 123

Locations (200m apart) Pavement Conditions Encroachment Comments

Nagan Chowrangi to C.O.D. Flyover

Intersection with R.A. Jafri Road (Dayyar e Shereen)

1 2 100m before Café Allah o Akbar 1 2 100m before CNG Station on right 2 2 Hashim Khan Quetta Hotel 3 3 Cross Sohrab Goth

4 Seasons Banquet 1 1 Just before Honda Showroom 1 2 Just after Magna Mall 1 1 Traffic Island speed breaker in 1 1 front of COD Lavish Dine

Sohrab Goth to K.P.T. Interchange Mamji Hospital 2 3 Anarkali Bazaar 3 5 Askari Bank 2 4 Mehfooz Lawn (opposite) 2 2 Shahbaz Motors 1 1 Levis Outlet Store Ayesha Manzil

Page 89 of 123

Ali Square 1 1 Point CNG (opposite) 1 1 The road just after the ground opposite Point CNG (+50m) 1 1 Aerosoft World (opposite) 3 1 Road after ending of complex (left) 4 3 Bank Al-Islami (opposite) 1 1 Al-Prince Market Cross Liaquatabad 10 number (flyover) Sindh Bank (opposite) 3 2 Meezan Bank (opposite) 1 3 2nd road after Firdos Shopping Center 1 3 PSO Pump (opposite) 1 2 Laloo Khait Cross Laloo Khait Baloch Masjid (opposite) 3 2 Lyari River 3 4 Mazar Noori Shah (opposite) 1 2 Caltex Petrol Pump (opposite) 1 2 Alim Engineering (Cooling tower) 1 3 Baloch Masjid (opposite) 1 3 Hascol Petrol Station 2 2 After Masjid Faizan Siddique Akbar (opposite) 3 3 Junaidi Air Travels and Tours 3 3 Gurumandir

Page 90 of 123

Go to Prince Cinema Prince Cinema 1 2 Italiano Pizza 3 1 Bank Alfalah 3 5 Pedestrian Bridge 1 3 Mama Parsi School (Mid) 2 3 NJV School 2 1 Soneri Bank 2 1 Dilpasand Sweets (opposite)

Locations (200m apart) Pavement Conditions Encroachment Comments

K.P.T. Interchange to Sohrab Goth Gul Plaza 4 3 Standard Chartered 3 2 Prince Cinema 2 1 Caltex Station 1 1 Taj Medical Go to Prince Cinema Laloo Khait 3 5 PSO Pump 4 5 2nd road after pedestrian crossing (opposite) 4 5 Meezan Bank 4 5 Sindh Bank

Page 91 of 123

Appendix I: Number and Width of Lanes of Selected Roads (Static Factors)

Page 92 of 123

STATIC FACTORS

UNIVERSITY ROAD

Direction 1: Jail Chowrangi to Safoora

S.No Arterials Location No. of Lanes Lane Width (m)

1- University Road Jail Chowrangi 3 11.51 Wildlife Aquarium 4 12.45 Babar Hospital (Right) 3 10.13 PIA Garden 3 12.07 Bank Al-Islami (Left) 3 9.05 Sir Syed University 3 10.77 Lalazar Banquet (Left) 3 10.21 Usman Institute of Technology

Direction 2: Safoora to Jail Chowrangi Usman Institute of University Road Technology 3 10.62 Lalazar Banquet (Left) 3 11.58 Sir Syed University 3 10.4 Bank Al-Islami (Left) 3 12.19 PIA Garden 3 10.64 Babar Hospital (Right) 3 10.28 Wildlife Aquarium 4 13.31 Jail Chowrangi

Page 93 of 123

RASHID MINHAS ROAD Direction 1: Bar B Q & Roll Point to C.O.D. Bridge 2- Rashid Minhas Road Bar B.Q and Roll Point 3 10.93 Dayyar e Shereen (intersection with Raees Ahmed Jafri Road) 2 6.6 Hashim Khan Quetta Hotel 3 11.49 Edhi Sard Khana 2 7.06 Fazal Mill 3 11.46 UBL Sports Complex 3 11.07 Shabbir Ahmed Usmani Flyover 2 7.01 NIPA

3 11.02 Aladin Park 3 11.14 Four Seasons Banquet 3 11.58

Lavish Dine

2 8.01 C.O.D Lawn 3 11.01 C.O.D Flyover

Page 94 of 123

Direction 2: C.O.D. Flyover to Nagan Chowrangi Flyover Rashid Minhas Road C.O.D Flyover 3 11.65 C.O.D Lawn 3 11.11

Lavish Dine

2 8.98 Four Seasons Banquet 3 10.87

Aladin Park

3 11.39 NIPA

3 10.69 Shabbir Ahmed Usmani Flyover 2 8.95 UBL Sports Complex 3 12.05 Fazal Mill 3 11.31 Edhi Sard Khana 2 7.51 Hashim Khan Quetta Hotel 3 11.5 Dayyar e Shereen (intersection with Raees Ahmed Jafri Road) 2 7.1 Bar B.Q and Roll Point

Page 95 of 123

SHAHRAH-E-PAKISTAN TO JAMSHED ROAD TO M.A JINNAH ROAD

Direction 1: M.A Jinnah Road to Sohrab Goth 3- Shahrah-e-Pakistan Gul Plaza 2 7.7 Taj Medical 2 7.05 Numaish 3 11.18 Gurumandir 2 6.51 Teen Hatti 3 11.08 Laloo Khait 3 12.02 Sindh Bank 3 11.35 Ahmed BBQ 2 9.51 Habib Medical Center 3 10.94 Ali Square 2 8.62 Naseerabad 3 11.19 Mamji Hospital 4 13.64 Sohrab Goth

Page 96 of 123

Direction 2: Sohrab Goth to M.A Jinnah Shahrah-e-Pakistan Sohrab Goth 2 6.85 Mamji Hospital 3 9.28 Naseerabad 3 11.31 Ali Square 3 9.51 Habib Medical Center 3 12.01 Ahmed BBQ 3 11.52 Sindh Bank 3 10.21 Laloo Khait 3 11.06 Teen Hatti Bridge 3 10.23 Baloch Masjid 2 7.05 Gurumandir 3 11.41 Numaish

3 11.01 Prince Cinema 3 11.06 Dilpasand Sweets

Page 97 of 123

SHER SHAH SURI ROAD - NAWAB SIDDIQUE ALI KHAN ROAD

Direction 1: Nagan Chowrangi to Gulbahar No.2 4- Sher Shah Suri Road Nagan Chowrangi 3 12.68 Erum Shopping Mall 3 11.19 Serena Mobile Mall 3 9.95 Farooq Azam Masjid 3 10.13 5 Star Chowrangi 3 11.03 Hyderi 3 10.18 KDA Chowrangi 3 12.2 Burger King 3 11.38 Abbasi Shaheed 2 8.77 Meezan Bank 3 11.17 Dow Lab 3 11.08 Firdous Colony Post Office 3 12.06 Gulbahar No. 2

Page 98 of 123

Direction 2: Gulbahar No.2 to Nagan Chowrangi Sher Shah Suri Road Gulbahar No. 2 3 10.03 Firdous Colony Post Office 4 12.21 Dow Lab 3 11.66 Meezan Bank 3 10.96 Abbasi Shaheed 3 11.79 Burger King 3 9.83 KDA Chowrangi 4 13.72 Hyderi 3 11.03 5 Star Chowrangi 3 12.02 Farooq Azam Masjid 3 12.25 Serena Mobile Mall 3 11.08 Erum Shopping Mall 3 12.66 Nagan Chowrangi

Page 99 of 123

SHAHRAH-E-FAISAL

Direction 1: Mehran Hotel to Malir Halt 5- Shahrah-e-Faisal Mehran Hotel 3 10.91

Mosque after Regent Plaza

3 11.38 FTC Building 4 13.45 Nursery Masjid 3 11.48 Pak Qatar Takaful 3 11.18 Pedestrian crossing before Baloch Colony 3 11.21 Tulips Marriage Hall 3 10.47 Just after Karsaz Flyover 3 11.39 PAF Base Montessori School 3 10.61 Master Apollo Motors 3 10.22 NHA Office 3 11.17 Bridge after Byco Petrol Pump 3 12.09 Attock Petrol Pump 3 11.26 Karachi Public School 3 10.51 Petrol Pump after Star Gate 2 7.28

Malir Halt

Page 100 of 123

Direction 2: Malir Halt to Mehran Hotel Shahrah-e-Faisal Malir Halt 2 7.61 Petrol Pump after Star Gate 3 10.25 Karachi Public School 3 11.35 Attock Petrol Pump 3 12.89 Bridge after Byco Petrol Pump 3 11.61 NHA Office 3 10.55 Master Apollo Motors 3 10.81 PAF Base Montessori School 3 11.22 Just after Karsaz Flyover 3 10.98 Tulips Marriage Hall 3 11.15 Pedestrian crossing before Baloch Colony 3 11.06 Pak Qatar Takaful 3 10.69 Nursery Masjid 4 13.68 FTC Building 3 11.11

Mosque after Regent Plaza

3 10.65 Mehran Hotel

Page 101 of 123

Appendix J: Land Use (Static Factors)

Page 102 of 123

LAND-USE

UNIVERSITY ROAD Location Direction Road Land-use Types

Commercial Open Jail Chowrangi - Wildlife Aquarium Residential Commercial Institutional + Recreational space Residential

jail 218.74 showrooms 170.83 aashi aprtmnt + shops 109.56 Jail Chowrangi to Safoora University Road shops 78.02 shops 67.04 shops + flats 74.11 wild life park 141.86

3 2 2 1.5

Commercial Open Wildlife Aquarium - Babar Hospital Residential Commercial Institutional + Recreational space Jail Chowrangi to Safoora University Road Residential askari park 301.16 resd + shops 201.93 174.86 shops+ hotel open area 98.81

2 1 2 3

Commercial Open Babar Hospital - PIA Garden Residential Commercial Institutional + Recreational space Residential mosque Jail Chowrangi to Safoora University Road 197.32m open space 138.76 shops 147.47 apprt + shops 174.09 apprt + shops 141.67

1.5 1.5 2 3

Commercial Open PIA Garden - Bank Al-Islami Residential Commercial Institutional + Recreational space Residential Jail Chowrangi to Safoora University Road shops resturant 185.3 shops 219.71 open area 109.46

4 1

Commercial Open Sir Syed University - Bank Al-Islami Residential Commercial Institutional + Recreational space Residential

Sir Syed University 250.88 Safoora to Jail Chowrangi University Road alig instit 229.07 apprt + shops 154.57 flats 195.47 shops+ flats 155.35 shops 133.49

2 1.5 4.5 3 Note: The values represent the covered area of the different types of land uses in square metres.

Page 103 of 123

Commercial Open Bank Al-Islami - PIA Garden Residential Commercial Institutional + Recreational space Residential ground 223.48 park 154.16 Safoora to Jail Chowrangi University Road ground 102.18 mosque 174.48 shops banks 205.19 shops 114.53 shops 106.46 PIA 218.32

4 3 2 3.5

Commercial Open PIA Garden - Babar Hospital Residential Commercial Institutional + Recreational space Residential expo Safoora to Jail Chowrangi University Road 186.37 civic center 161.52 district corporate east 99.81 petrol pump 41.34 hosp 34.59

3 0.5 2

RASHID MINHAS ROAD

Commercial Open Lavish Dine - Four Seasons Banquet Residential Commercial Institutional + Recreational space Residential lavish dine 26.35 shops C.O.D. Flyover to Nagan Rashid Minhas 37.22 millinieum mall Chowrangi Flyover Road 182.53 magna mall 114.34 showroom+ flats 63.13 flats 101.13 ground 66.7 marriage hall 51.15

1 4 0.5 0.5 0.5

Commercial Hashim Khan Quetta Hotel - Open Residential Commercial Institutional + Recreational Intersection w/ R. A. Jafri Rd. space Residential C.O.D. Flyover to Nagan Rashid Minhas Chowrangi Flyover Road hotel 55.8 homes 122.21 homes 137.34 shops homes 115.94 homes 101.35

3.5 0.5 1 Note: The values represent the covered area of the different types of land uses in square metres.

Page 104 of 123

Commercial Open BBQ and Roll Point - Nagan Chowrangi Residential Commercial Institutional + Recreational space Residential hotel 45.96 C.O.D. Flyover to Nagan Rashid Minhas hotel 88.1 Chowrangi Flyover Road petrol pump 42.17 mechanic shop+workshop 114.14 homes 94.55 homes 90.76 shop 33.64

2 3

Commercial Intersection w/ R. A. Jafri Rd. - Hashim Open Residential Commercial Institutional + Recreational Khan Quetta Hotel space Residential

Nagan Chowrangi Flyover Rashid Minhas to C.O.D Flyover Road mosque 53.39 petrol pump 65.91 homes 104.42 homes 109.55 open area 87.47 petrol pump 50.41 flats 89.87

3 1 1 0.5

Commercial Open Four Seasons Banquet - Lavish Dine Residential Commercial Institutional + Recreational space Residential Nagan Chowrangi Flyover Rashid Minhas shops+flats 78.08 to C.O.D Flyover Road shops+flats 69.79 flats 72.7 60.84 flats shops 58.22 ground 144.8 petrol pump 107.27 open area 99.26

1 1.5 2..5 1.5

Commercial Open C.O.D. Lawn - C.O.D. Flyover Nagan Chowrangi Flyover Rashid Minhas Residential Commercial Institutional + Recreational space to C.O.D Flyover Road Residential open area 485.58

5 Note: The values represent the covered area of the different types of land uses in square metres.

Page 105 of 123

M.A JINNAH ROAD

Commercial Open Prince Cinema - Dilpasand Sweets Residential Commercial Institutional + Recreational space Residential hosp 173.61 naz plaza 87.36 flats +shops Gurumandir to KPT M.A Jinnah 75.28 shops Flyover Road 98.8 showroom 61.63 shops 245.44 shops 118.03 flats +shops 168.01 hosp 75.9 shops+hotel 159.54

5 2.5 2.5

Commercial Open Gul Plaza - Taj Medical Residential Commercial Institutional + Recreational space Residential KPT Flyover to M.A Jinnah shopping center Gurumandir Road 71.88 shops 178.43 flats+shops 175.39 shops 120.81

3.5 2

SHAHRAH-E-PAKISTAN

Commercial Open Laloo Khait - Sindh Bank Residential Commercial Institutional + Recreational space Residential Teen Hatti Bridge to Shahrah-e- shops Sohrab Goth Pakistan 144.56 flats + shops 287.6 flats + shops 128.08 shops 109.98

2.5 4

Commercial Open Mamji Hospital - Naseerabad Residential Commercial Institutional + Recreational space Residential flats 149.17 Sohrab Goth to Teen Hatti Shahrah-e- shops 34.94 Bridge Pakistan market 93.07 shops 89.55 shops+flats 136.66 shops+flats 69.5 shops+flats 239.67

1.5 2 4.5

Note: The values represent the covered area of the different types of land uses in square metres.

Page 106 of 123

Commercial Open Ali Square - Habib Medical Center Residential Commercial Institutional + Recreational space Residential flats Sohrab Goth to Teen Hatti Shahrah-e- 80.89 school Bridge Pakistan 276.06 jamat khana 137.82 resd colony 280.9 flats+shops 293.93 shopping market 79.46

3.5 1 4 3

Commercial Open Sindh Bank - Laloo Khait Residential Commercial Institutional + Recreational space Residential Sohrab Goth to Teen Hatti Shahrah-e- school Bridge Pakistan 101.2 shop 66.05 flats 299.78 shop 66.46 flats+shops 179.26 70 petrol pump police station 83.4 shops 165.09

3 3.5 2 2

Note: The values represent the covered area of the different types of land uses in square metres.

Page 107 of 123

Appendix K: Driver Behavior (Dynamic Factors)

Page 108 of 123

Raza Raza RM1-00035 RM1-00035 Fast Lane Score Slow Lane Score

Car Truck Car Truck Bus Bus (passenger (hiace, (passenger (hiace, Rickshaw/ (minibus, Raw Rickshaw/ (minibus, Raw Minutes car, hi-roof, Bike hilux, Minutes car, hi-roof, Bike hilux, qingqi large Score qingqi large Score Suzuki pick- larger Suzuki pick- larger bus) bus) up), trucks) up), trucks) 0-5 34 58 21 7 0 120 0-5 7 38 17 1 3 66 5 to 10 44 71 17 4 0 136 5 to 10 12 51 18 1 2 84 10 to 15 41 71 27 4 4 147 10 to 15 4 25 15 0 3 47 15 to 20 45 60 25 4 1 135 15 to 20 8 42 17 0 3 70 20 to 24:36 29 58 11 8 2 108 20 to 24:36 5 21 16 2 6 50

Raza Raza Truck stopped for first 1:45 RM1-00036 RM1-00036 Fast Lane Score Slow Lane Score Car Truck Car Truck Bus Bus (passenger (hiace, (passenger (hiace, Rickshaw/ (minibus, Raw Rickshaw/ (minibus, Raw Minutes car, hi-roof, Bike hilux, Minutes car, hi-roof, Bike hilux, qingqi large Score qingqi large Score Suzuki pick- larger Suzuki pick- larger bus) bus) up), trucks) up), trucks) 0-5 33 74 11 10 1 129 0-5 5 35 28 2 2 72 5 to 10 41 75 13 6 7 142 5 to 10 6 39 19 3 2 69 10 to 15 39 80 18 4 3 144 10 to 15 10 36 16 0 1 63 15 to 17:47 17 41 8 2 2 70 15 to 17:47 2 34 17 1 0 54

Raza Raza RM1-00037 RM1-00037 Fast Lane Score Slow Lane Score

Car Truck Car Truck Bus Bus (passenger (hiace, (passenger (hiace, Rickshaw/ (minibus, Raw Rickshaw/ (minibus, Raw Minutes car, hi-roof, Bike hilux, Minutes car, hi-roof, Bike hilux, qingqi large Score qingqi large Score Suzuki pick- larger Suzuki pick- larger bus) bus) up), trucks) up), trucks)

0-5 32 72 13 1 7 125 0-5 3 30 12 45 5 to 10 39 56 8 4 3 110 5 to 10 9 25 14 39 10 to 15 39 79 14 3 4 139 10 to 15 4 34 10 48 15 to 20 49 71 11 3 1 135 15 to 20 6 32 8 46 20 to 25 38 72 10 8 1 129 20 to 25 11 55 18 84 25 to 30:25 49 90 12 6 1 158 25 to 30:25 18 45 15 78

Note: The caption on top of each table is the name of the person responsible for recording the video, followed by the name of the video as saved in the computer.

The score was calculated according to the table below.

Action Score Driving between lanes 1

Lane Changes Affecting fast lane 2 Affecting slow lane 1 Page 109 of 123

Appendix L: Traffic Counts

Page 110 of 123

TIMES CARS BUSES/TRUCKS BIKES RICKSHAW/QINCHI Total

11:00 0 0 0 0 11:05 148 27 346 61 582 11:10 160 20 281 49 510 11:15 152 17 255 47 471 11:20 139 17 254 67 477 11:25 153 26 237 49 465 11:30 115 15 193 68 391 11:35 154 20 198 57 429 11:40 136 11 184 52 383 11:45 122 20 212 53 407 11:50 113 20 156 52 341 11:55 140 25 219 55 439 12:00 170 22 194 53 439 12:05 172 11 201 63 447 12:10 184 17 207 70 478 12:15 205 17 210 62 494 12:20 193 19 202 53 467 12:25 158 25 206 56 445 12:30 163 21 183 50 417 12:35 184 13 191 49 437 12:40 114 18 192 53 377 12:45 158 24 237 52 471 12:50 142 24 202 58 426

Gulshan-e-Iqbal

TIMES CARS BUSES/TRUCKS BIKES RICKSHAW/QINCHI 11:30 0 0 0 0 11:35 116 16 194 54 380 11:40 91 19 168 41 319 11:45 115 18 163 50 346 11:50 79 13 179 77 348 11:55 95 20 175 67 357 12:00 98 22 183 63 366 12:05 104 21 176 57 358 12:10 124 12 167 57 360 12:15 96 14 202 48 360 12:20 100 15 189 60 364 12:25 106 14 178 49 347 12:30 109 30 192 49 380 12:35 103 16 209 54 382 12:40 100 22 230 40 392 12:45 95 11 232 54 392 12:50 110 16 262 52 440

Shafique Mor

Page 111 of 123

TIMES CARS BUSES/TRUCKS BIKES RICKSHAW/QINCHI Total 10:30 0 0 0 0 10:35 211 23 261 71 566 10:40 214 22 287 73 596 10:45 153 12 231 71 467 10:50 198 11 246 80 535

10:55 185 12 201 50 448 11:00 192 17 260 68 537

11:05 174 21 256 78 529 11:10 214 21 247 64 546

11:15 91 10 148 40 289 11:20 166 19 247 43 475 11:25 173 22 241 72 508 11:30 194 17 290 61 562 11:35 181 15 256 64 516 11:40 217 23 287 75 602 11:45 209 18 310 70 607 11:50 186 23 274 72 555 11:55 179 12 270 48 509 12:00 190 26 277 66 559 12:05 224 12 278 72 586 12:10 225 23 263 62 573 12:15 236 24 338 70 668 12:20 219 19 293 54 585 12:25 258 19 333 60 670 12:30 233 17 273 76 599 12:35 223 22 329 64 638 12:40 234 19 419 80 752 12:45 225 16 342 78 661 12:50 253 15 393 65 726

Aladin Park

Page 112 of 123

Appendix M: Speed Observations for University Road

Page 113 of 123

5

36

34

42

44

58

44

58

58

41

45

58

57

0

28

38

42

28

62

66

58

56

40

56 62

46

Segm(5-4) Segm(3-1)

5

43

32

40

44

58

42

59

58

39

43

59

55

31

40

24

38

38

61

65

58

54

40

54

66 45

Segm(5-3) Segm(2-4)

0

46

33

36

46

57

42

58

57

38

43

61

55

Segm(5-2)

32

43

34

34

42

60

64

56

54

38

53 63

44

Segm(2-3)

48

24

32

26

46

60

46

56

57

40

48 61

53

Segm(5-1)

30

41

37

30

44

58

63

53

52

38

53 62

42

Segm(2-2)

44

24

32

30

46

64

49

52

56

42

52

61 53

(Km/hr)

Segm(4-4)

31

39

34

28

48

56

62

53

51

44

54 64

41

40

30

29

37

44

66

52

53

56

43

50

60

52 Segm(2-1)

Segm(4-3)

(Km/hr)

34

34

33

32

54

53

60

52

50

41

55 63

40

34

36

26

43

44

68

58

53

57

44

48

58

52 Segm(1-4)

Segm(4-2)

26

32

36

38

60

51

60

54

49

37

55

61

39

24

30

30

45

44

67

64

54

56

45

50 57

52 Segm(4-1)

Segm(1-3)

27

22

22

42

39

66

67

54

56

46

52

56

50

5

34

39

44

60

49

59

56

47

32

56 59

36 Segm(3-4)

Segm(1-2)

32

22

26

40

36

65

67

58

56

44

53

56 50

0

33

42

46

60

46

59

58

44

48

57

58

32 Segm(3-3)

Segm(1-1)

32

30

20

42

32

64

66

59

56

40

58

59 48

Segm(3-2)

6.500

6.000

5.500

5.000

4.500

4.000

3.500

3.000

2.500

2.000

1.500

1.000

0.500 End(km)

6.500

6.000

5.500

5.000

4.500

4.000

3.500

3.000

2.500

2.000

1.500

1.000

0.500

End(km)

6.000

5.500

5.000

4.500

4.000

3.500

3.000

2.500

2.000

1.500

1.000 0.500

0.000

Start(km)

6.000

5.500

5.000

4.500

4.000

3.500

3.000

2.500

2.000

1.500

1.000

0.500 0.000

Start(km)

Section ID

Section#09 (I-J) Section#09

Section ID

Section#11 (K-L) Section#11

Section#10 (J-K) Section#10

Section#08 (H-I) Section#08

Section#05 (E-F) Section#05

Section#06 (F-G) Section#06

Section#04 (D-E) Section#04

Section#02 (B-C) Section#02

Section#12 (L-M) Section#12

Section#07 (G-H) Section#07

Section#03 (C-D) Section#03

Section#01 (A-B) Section#01

Section#13 (M-N) Section#13

Section#09 (I-J) Section#09

Section#11 (K-L) Section#11

Section#10 (J-K) Section#10

Section#08 (H-I) Section#08

Section#05 (E-F) Section#05

Section#06 (F-G) Section#06

Section#04 (D-E) Section#04

Section#02 (B-C) Section#02

Section#12 (L-M) Section#12

Section#07 (G-H) Section#07

Section#03 (C-D) Section#03

Section#01 (A-B) Section#01 Section#13 (M-N) Section#13

9

8

7

6

5

4

3

2

1

13

12

11

10

9

8

7

6

5

4

3

2

1

13

12

11

10 S no : no S

: no S

SPEED OBSERVATIONS SPEED OBSERVATIONS Page 114 of 123

67

64

60

55

60

54

63

37

43

30

30 27

37

60

71

65

61

54

52

40

48

26

30

38

29

40 Segm(5-4)

Segm(3-1)

68

63

58

55

58

57

61

48

43

28

42 22

32

59

70

66

61

51

51

36

46

40

29

33

31

40 Segm(5-3)

Segm(2-4)

8

68

61

58

53

58

60

60

55

45

32

43 30

Segm(5-2)

58

70

65

61

49

50

32

45

43

23

31

32 37

Segm(2-3)

68

61

60

53

56

61

59

55

44

32

42

10 30

Segm(5-1)

58

69

64

61

46

52

28

44

42

28

30

34 32

Segm(2-2)

70

62

59

55

55

61

57

54

40

30

42

26

40

(Km/hr)

Segm(4-4)

63

69

62

63

42

58

23

45

40

28

33

38

32

70

66

57

55

56

60

56

53

38

24

42

29

40

Segm(2-1)

Segm(4-3)

(Km/hr)

64

67

61

63

42

60

10

46

41

26

30

40

28

70

70

56

52

60

58

55

52

38

22

40

33

38

Segm(1-4)

Segm(4-2)

0

66

65

59

62

46

60

47

44

26

30

38

20

68

72

54

50

61

56

53

50

34

21

38

34

44

Segm(4-1)

Segm(1-3)

66

74

54

52

60

55

50

50

28

25

38

30

46

66

64

58

61

48

61

24

47

40

30

31

35

10

Segm(3-4)

Segm(1-2)

3

65

73

57

58

58

52

48

49

30

40

33

44

5

65

62

57

60

54

62

30

43

36

30

30

37

Segm(3-3)

Segm(1-1)

5

63

72

61

60

56

52

46

48

31

40

31

42

Segm(3-2)

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

5.000

5.500

6.000

End(km)

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

5.000

5.500

6.000

End(km)

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

5.000

5.500

6.000

6.500

Start(km)

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

5.000

5.500

6.000

6.500

Start(km)

Section#09 (J-I) Section#09

Section#05 (F-E) Section#05

Section#08 (I-H) Section#08

Section#10 (K-J) Section#10

Section#11 (L-K) Section#11

Section#02 (C-B) Section#02

Section#04 (E-D) Section#04

Section#06 (G-F) Section#06

Section#01 (B-A) Section#01

Section#03 (D-C) Section#03

Section#07 (H-G) Section#07

Section#12 (M-L) Section#12

Section#13 (N-M) Section#13

Section ID

Section#09 (J-I) Section#09

Section#05 (F-E) Section#05

Section#08 (I-H) Section#08

Section#10 (K-J) Section#10

Section#11 (L-K) Section#11

Section#02 (C-B) Section#02

Section#04 (E-D) Section#04

Section#06 (G-F) Section#06

Section#01 (B-A) Section#01

Section#03 (D-C) Section#03

Section#07 (H-G) Section#07

Section#12 (M-L) Section#12

Section#13 (N-M) Section#13

Section ID

9

8

7

6

5

4

3

2

1

13

12

11

10

9

8

7

6

5

4

3

2

1

13

12

11

10

S no : no S

S no : no S

SPEED OBSERVATIONS SPEED OBSERVATIONS Page 115 of 123

Appendix N: Financial Statement

Page 116 of 123

Page 117 of 123