ned university of engineering and technology

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

mailto:[email protected]

mailto:[email protected]

Page ii of 123

Page iii of 123

Page iv 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

Professor / PI* Dr. Sana Muqeem

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 v 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 vi 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

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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.

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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.

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0 5000 10000 15000 20000 25000

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Total Persons

Figure 1.2.1: A time-based comparison of four stretches of Shahra-

e-Faisal with respect to total persons traveling.

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Fig 1.2.2: A time-based comparison of four stretches of Shahra-e-

Faisal with respect to total persons traveling.

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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.

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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)

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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.

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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.

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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.”

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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)

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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.

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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

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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

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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

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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)

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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

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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.

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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.

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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

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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

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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.

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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

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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.

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

Peak

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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

Design capacity of road Driving behavior (aggressive, risk-averse

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

intervals VIP movement and security checks

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.

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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.

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

9 Absence/improper implementation of functional

classification of roads 0.80 7

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

Neglected

17 No. of lanes 0.63 12

18 Weather condition 0.60 13

19 VIP movement and security checks 0.60 13

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Jail Chowrangi to Safoora

Landmark (~ 1 km apart)

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

Light 1 Heavy congestion till Urdu college stop from 4 to 9, thins out steeply onward

Saturated

Heavy

Very Heavy

University RoadDirection:

Time

No data available after University Lawn Banquet Hall

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).

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

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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

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).

Fig. 2.4.5.1. Pavement Condition Index of University Road (in 25m sections from

Nipa to Safoora)

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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

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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)

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Raza

RM1-00035

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 7 38 17 1 3 66

5 to 10 12 51 18 1 2 84

10 to 15 4 25 15 0 3 47

15 to 20 8 42 17 0 3 70

20 to 24:36 5 21 16 2 6 50

Raza Truck stopped for first 1:45

RM1-00036

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 5 35 28 2 2 72

5 to 10 6 39 19 3 2 69

10 to 15 10 36 16 0 1 63

15 to 17:47 2 34 17 1 0 54

Raza

RM1-00037

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 3 30 12 45

5 to 10 9 25 14 39

10 to 15 4 34 10 48

15 to 20 6 32 8 46

20 to 25 11 55 18 84

25 to 30:25 18 45 15 78

Slow Lane Score

Slow Lane Score

Slow Lane Score

Raza

RM1-00035

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 34 58 21 7 0 120

5 to 10 44 71 17 4 0 136

10 to 15 41 71 27 4 4 147

15 to 20 45 60 25 4 1 135

20 to 24:36 29 58 11 8 2 108

Raza

RM1-00036

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 33 74 11 10 1 129

5 to 10 41 75 13 6 7 142

10 to 15 39 80 18 4 3 144

15 to 17:47 17 41 8 2 2 70

Raza

RM1-00037

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 32 72 13 1 7 125

5 to 10 39 56 8 4 3 110

10 to 15 39 79 14 3 4 139

15 to 20 49 71 11 3 1 135

20 to 25 38 72 10 8 1 129

25 to 30:25 49 90 12 6 1 158

Fast Lane Score

Fast Lane Score

Fast Lane Score

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

Fig. 2.5.1.4. Computing a score for vehicle-specific driver behavior for the fast and

slow lanes of one direction of Rashid Minhas Road (near Aladin Park)

Traffic counts

Traffic count was observed for different types of modes i.e. bus, trucks, cars,

motorbikes at five-minute interval at the three different locations on Rashid Minhas

Road (detailed in Appendix L).

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.

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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

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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.

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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

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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(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.

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Table 2.6.2.1: Influencing Factors Coding

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

F9 Absence/improper implementation of

functional classification of roads

F10 On-street parking

F11 Bottlenecks (work zones etc.)

F12 Presence of road intersection at small

intervals

F13 vehicular mix (too many trucks and cars)

F14 Poor road design (narrow lanes etc.)

F15 No. of lanes

TS Travel Speed

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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

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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

static F1 Encroachment and

poor enforcement Scale 1 to 5

1= Minimum lane occupied,

5= Maximum Lane Occupied

static F2 Lack of public

transport Scale 1 to 5

1=maximum number of buses ,

5= minimum number of buses

dynamic F3 Traffic volume on

the road

No of vehicles

passed

static F4

Land use of the

area under

consideration

Scale 1 to 5

1= Residential, 2= Residential

+ commercial, 3=

Recreational, 4= Educational,

5= Commercial

static F5 Pavement

condition Scale 1 to 5

1= Excellent condition, 5=

worst Condition

static F6

Ease in buying

vehicles (car

leasing etc.)

Scale 1 to 5 1= most difficult, 5= most easy

static F7

Poor signal design

and

synchronization

Scale 1 to 5 1= good design, 5= Poor

Design

dynamic F8 Driving behavior % of vehicles

change lane

vehicle change/traffic count *

100

static F9

Absence/improper

implementation of

functional

classification of

road

Scale 1 to 5 1= proper classification, 5=

improper classification

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dynamic F10 On-street parking No. of Vehicles

parked

static F11 Bottlenecks (work

zones etc.) Scale 1 to 5

1= minimum lane width drops,

5= maximum lane width drop

static F12

Presence of road

intersection at

small intervals

Scale 1 to 5 1= small no. of intersection, 5=

large number of intersection

dynamic F13

vehicular mix (too

many trucks and

cars)

% of T/B (5-30%)

static F14 Poor road design

(narrow lanes etc.) Scale 1 to 5

1= good design, 5= Poor

Design

static F15 No. of lanes number of lanes

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

100km/hr

Table 2.6.2.3: Unit of Measurement of Influencing Factors

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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.

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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

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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

2.8.3 Expendable supplies

1. Field work expenses

2. Data Extraction

3. Journal Publication Fee

4. Stationery/Contingency

5. Communication

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

Video Recording

System (2 Cameras +

1 DVR)

Traffic Volume Counts,

since DVR is included in

item below

High-definition camera and mount (Price =

approximately Rs. 50,000/-)1

100,000 (50,000

for camera,

50,000 for DVR)

Electronic Distance

Measuring Tool (2)

Recording Location

Parameters

Car black box with GPS and DVR. This device can record

the vehicle speed and station (location) along with its

function as a DVR. Requires a power supply (Price =

approximately Rs. 8,000/-)

1190,000 (95,000

each)

http://www.alibaba.com/product-

detail/4ch-black-box-3g-car-

dvr_692629041.html?spm=a2700.7724

857.0.0.htg6pO

Laser Gun

Short Distance

Measurements

(headways, distance

covered by vehicle etc.)

Personal trackers, since they can transmit location,

distance and speed data much more conveniently. They

can also be used by several people simultaneously, and

in vehicles that lack a power supply. (Price =

approximately Rs. 70,000/-)

5 200,000http://trackimo.com/ 0315-3671360

0322-2407068

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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)

1 TRANSPORT DEVELOPMENT IN ASIAN MEGACITIESShigeru Morichi, Surya Raj

Acharya

Springer Berlin

Heidelberg978-3-642-29742-7 6,128

ISBN-10:

387758569

ISBN-13:

ISBN-10: 0415285151

ISBN-13: 978-

0415285155

ISBN-13: 978-1627052078

ISBN-10: 9067641715

8ROAD TRAFFIC CONGESTION: A CONCISE GUIDE,

VOLUME 7 2015

Authors: John C.

Falcocchio, Herbert S.

Levinson

Springer

ISBN: 978-3-319-15164-9

(Print) 978-3-319-15165-6

(Online)

11,656

17,5007ECONOMICS OF URBAN HIGHWAY CONGESTION AND

PRICING

McDonald, J. F., D'ouville,

Edmond L., Louie Nan LiuSpringer ISBN 978-1-4615-5231-4

12,000

6ARTIFICIAL INTELLIGENCE APPLICATIONS TO TRAFFIC

ENGINEERING 1ST EDITIONCRC Press 6,000

5

INTRODUCTION TO INTELLIGENT SYSTEMS IN TRAFFIC

AND TRANSPORTATION (SYNTHESIS LECTURES ON

ARTIFICIAL INTELLIGENCE AND MACHINE

LEARNING) 1ST EDITION

Morgan and Claypool

Publishers4,000

Ana L. C. Bazzan,

Franziska Klügl

CRC Press

2

TRANSPORTATION SYSTEMS ANALYSIS: MODELS AND

APPLICATIONS (SPRINGER OPTIMIZATION AND ITS

APPLICATIONS)/2ND EDITION

Ennio Cascetta Springer; 2nd Edition

(September 15, 2009)16,000

3THE ECONOMICS OF URBAN TRANSPORTATION/2ND

EDITION

Kenneth Small, Erik

Verhoef

Routledge; 2nd Edition

(November 15, 2007)6,128

4SPATIAL ANALYSIS METHODS OF ROAD TRAFFIC

COLLISIONS

Becky P. Y. Loo, Tessa

Kate Anderson

Bielli (Editor), Ambrosino (E

ditor), Boero (Editor)

978-0387758565

ISBN-13: 978-9067641715

ISBN-10: 1627052070

978-3-642-29742-7

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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.

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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.

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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

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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.

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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

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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.

of 123

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

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

of 123

Appendix A: Expert Opinion Form for Causes of Traffic

Congestion

of 123

of 123

Appendix B: Survey Form for Congestion on Arterials

of 123

of 123

Appendix C: Map of Selected Arterials of Karachi

of 123

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.

of 123

Appendix D: Congestion Chart

of 123

of 123

Appendix E: Plan for Recording Traffic Videos at Selected

Locations and Times

of 123

of 123

Appendix F: Pro formas

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LOCATION: SECTION (TO/FROM): DATE:

STATION NO. : DIRECTION: DAY:

ROAD NAME:

VEHICLES

TIME(min) BUS TRUCK CAR TOTAL

00:00-10:00

00:10-00:20

00:20-00:30

00:30-00:40

MOTORCYCLE/RICKSHAWS

TRAFFIC COUNT SURVEY

of 123

of 123

Appendix G: Relative Importance Index for Prioritizing

Factors

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Appendix H: Encroachment and Pavement Condition Data at

Selected Locations

Locations (200m apart) Pavement Condition Encroachment Comments

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


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

Jail Chowrangi to Bank Al-Islami

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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

Safoora to Jail Chowrangi

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Locations (200m apart) Pavement Conditions Encroachment Comments

Lavish Dine

2 1

Traffic Island

1 3

Just before Magna Mall

1 3

Just after Honda Showroom

1 4

4 Seasons Banquet

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)

1 1

BBQ and Roll Point (Left)

1 1

PSO Pump (Left)

1 1

Family Park (Right)

1 2

Nagan Chowrangi

C.O.D. Flyover to Nagan Chowrangi

Cross Sohrab Goth

Keep Going

of 123


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


3 3

4 Seasons Banquet

1 1

Just before Honda Showroom

1 2

Just after Magna Mall

1 1

Traffic Island

1 1speed breaker in

front of COD

Lavish Dine

Nagan Chowrangi to C.O.D. Flyover

Cross Sohrab Goth

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

Sohrab Goth to K.P.T. Interchange

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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

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

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

Cross Liaquatabad 10 number (flyover)

Cross Laloo Khait

of 123

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)

Go to Prince Cinema


Gul Plaza

4 3

Standard Chartered

3 2

Prince Cinema

2 1

Caltex Station

1 1

Taj Medical

Laloo Khait

3 5

PSO Pump

4 5

2nd road after pedestrian crossing

(opposite)

4 5

Meezan Bank

4 5

Sindh Bank

K.P.T. Interchange to Sohrab Goth

Go to Prince Cinema

of 123

Appendix I: Number and Width of Lanes of Selected Roads

(Static Factors)

of 123

Direction 1:

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

1- University Road Jail Chowrangi

3 11.51

Wildlife Aquarium

4 12.45


3 10.13

PIA Garden

3 12.07


3 9.05

Sir Syed University

3 10.77


3 10.21

Usman Institute of

Technology

Direction 2:

University RoadUsman Institute of

Technology

3 10.62


3 11.58

Sir Syed University

3 10.4


3 12.19

PIA Garden

3 10.64


3 10.28

Wildlife Aquarium

4 13.31

Jail Chowrangi

STATIC FACTORS

UNIVERSITY ROAD

Jail Chowrangi to Safoora

Safoora to Jail Chowrangi

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Direction 1:

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


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

RASHID MINHAS ROAD

Bar B Q & Roll Point to C.O.D. Bridge

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Direction 2:

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


3 11.5

Dayyar e Shereen

(intersection with Raees

Ahmed Jafri Road)

2 7.1

Bar B.Q and Roll Point

C.O.D. Flyover to Nagan Chowrangi Flyover

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Direction 1:

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

M.A Jinnah Road to Sohrab Goth

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

of 123

Direction 2:

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

Sohrab Goth to M.A Jinnah

of 123

Direction 1:

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

SHER SHAH SURI ROAD - NAWAB SIDDIQUE ALI KHAN ROAD

Nagan Chowrangi to Gulbahar No.2

of 123

Direction 2:

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

Gulbahar No.2 to Nagan Chowrangi

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Direction 1:

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

SHAHRAH-E-FAISAL

Mehran Hotel to Malir Halt

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Direction 2:

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

Malir Halt to Mehran Hotel

of 123

Appendix J: Land Use (Static Factors)

of 123

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

metres.

Location Direction Road

Jail Chowrangi - Wildlife Aquarium Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

jail 218.74

showrooms 170.83

aashi aprtmnt + shops 109.56

shops 78.02

shops 67.04

shops + flats 74.11

wild life park 141.86

3 2 2 1.5

LAND-USE

UNIVERSITY ROAD

Land-use Types

Jail Chowrangi to Safoora University Road

Wildlife Aquarium - Babar Hospital Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

askari park 301.16

resd + shops 201.93

174.86

open area 98.81

2 1 2 3


shops+ hotel

Babar Hospital - PIA Garden Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

mosque 197.32m

open space 138.76

shops 147.47

apprt + shops 174.09


1.5 1.5 2 3


PIA Garden - Bank Al-Islami Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

shops resturant 185.3

shops 219.71

open area 109.46

4 1


Sir Syed University - Bank Al-Islami Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

Sir Syed University 250.88

alig instit 229.07


flats 195.47

shops+ flats 155.35

shops 133.49

2 1.5 4.5 3

Safoora to Jail Chowrangi University Road

of 123


metres.

Bank Al-Islami - PIA Garden Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

ground 223.48

park 154.16

ground 102.18

mosque 174.48

shops banks 205.19

shops 114.53

shops 106.46

PIA 218.32

4 3 2 3.5


PIA Garden - Babar Hospital Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

expo 186.37

civic center 161.52

district corporate east 99.81

petrol pump 41.34

hosp 34.59

3 0.5 2


Lavish Dine - Four Seasons Banquet Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

lavish dine 26.35

shops 37.22

millinieum mall 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

RASHID MINHAS ROAD

C.O.D. Flyover to Nagan

Chowrangi Flyover

Rashid Minhas

Road

Hashim Khan Quetta Hotel -

Intersection w/ R. A. Jafri Rd.Residential Commercial

Open

spaceInstitutional

Commercial

+

Residential

Recreational

hotel 55.8

homes 122.21

homes 137.34

shops homes 115.94

homes 101.35

3.5 0.5 1


Chowrangi Flyover

Rashid Minhas

Road

of 123


metres.

BBQ and Roll Point - Nagan Chowrangi Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

hotel 45.96

hotel 88.1

petrol pump 42.17

mechanic shop+workshop 114.14

homes 94.55

homes 90.76

shop 33.64

2 3


Chowrangi Flyover

Rashid Minhas

Road

Intersection w/ R. A. Jafri Rd. - Hashim

Khan Quetta HotelResidential Commercial

Open

spaceInstitutional

Commercial

+

Residential

Recreational

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

Four Seasons Banquet - Lavish Dine Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

shops+flats 78.08

shops+flats 69.79

flats 72.7

60.84

shops 58.22

ground 144.8

petrol pump 107.27

open area 99.26

1 1.5 2..5 1.5

Nagan Chowrangi Flyover

to C.O.D Flyover

Rashid Minhas

Road

flats


to C.O.D Flyover

Rashid Minhas

Road

C.O.D. Lawn - C.O.D. Flyover Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

open area 485.58

5


to C.O.D Flyover

Rashid Minhas

Road

of 123


metres.

Prince Cinema - Dilpasand Sweets Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

hosp 173.61

naz plaza 87.36

flats +shops 75.28

shops 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

Gul Plaza - Taj Medical Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

shopping center 71.88

shops 178.43

flats+shops 175.39

shops 120.81

3.5 2

M.A JINNAH ROAD

Gurumandir to KPT

Flyover

M.A Jinnah

Road

KPT Flyover to

Gurumandir

M.A Jinnah

Road

Laloo Khait - Sindh Bank Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

shops 144.56

flats + shops 287.6

flats + shops 128.08

shops 109.98

2.5 4

Mamji Hospital - Naseerabad Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

flats 149.17

shops 34.94

market 93.07

shops 89.55

shops+flats 136.66

shops+flats 69.5

shops+flats 239.67

1.5 2 4.5

SHAHRAH-E-PAKISTAN

Teen Hatti Bridge to

Sohrab Goth

Shahrah-e-

Pakistan

Sohrab Goth to Teen Hatti

Bridge

Shahrah-e-

Pakistan

of 123


metres.

Ali Square - Habib Medical Center Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

flats 80.89

school 276.06

jamat khana 137.82

resd colony 280.9

flats+shops 293.93

shopping market 79.46

3.5 1 4 3

Sindh Bank - Laloo Khait Residential CommercialOpen

spaceInstitutional

Commercial

+

Residential

Recreational

school 101.2

shop 66.05

flats 299.78

shop 66.46

flats+shops 179.26

70

police station 83.4

shops 165.09

3 3.5 2 2

petrol pump


Bridge

Shahrah-e-

Pakistan


Bridge

Shahrah-e-

Pakistan

of 123

Appendix K: Driver Behavior (Dynamic Factors)

of 123

Raza

RM1-00035

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 7 38 17 1 3 66

5 to 10 12 51 18 1 2 84

10 to 15 4 25 15 0 3 47

15 to 20 8 42 17 0 3 70

20 to 24:36 5 21 16 2 6 50

Raza Truck stopped for first 1:45

RM1-00036

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 5 35 28 2 2 72

5 to 10 6 39 19 3 2 69

10 to 15 10 36 16 0 1 63

15 to 17:47 2 34 17 1 0 54

Raza

RM1-00037

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 3 30 12 45

5 to 10 9 25 14 39

10 to 15 4 34 10 48

15 to 20 6 32 8 46

20 to 25 11 55 18 84

25 to 30:25 18 45 15 78

Slow Lane Score

Slow Lane Score

Slow Lane Score

Raza

RM1-00035

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 34 58 21 7 0 120

5 to 10 44 71 17 4 0 136

10 to 15 41 71 27 4 4 147

15 to 20 45 60 25 4 1 135

20 to 24:36 29 58 11 8 2 108

Raza

RM1-00036

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 33 74 11 10 1 129

5 to 10 41 75 13 6 7 142

10 to 15 39 80 18 4 3 144

15 to 17:47 17 41 8 2 2 70

Raza

RM1-00037

Minutes

Car

(passenger

car, hi-roof,

Suzuki pick-

up),

BikeRickshaw/

qingqi

Truck

(hiace,

hilux,

larger

trucks)

Bus

(minibus,

large

bus)

Raw

Score

0-5 32 72 13 1 7 125

5 to 10 39 56 8 4 3 110

10 to 15 39 79 14 3 4 139

15 to 20 49 71 11 3 1 135

20 to 25 38 72 10 8 1 129

25 to 30:25 49 90 12 6 1 158

Fast Lane Score

Fast Lane Score

Fast Lane Score

Score

1

2

1Affecting slow lane

Lane Changes

Affecting fast lane

Action

Driving between lanes

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.

of 123

Appendix L: Traffic Counts

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

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

Gulshan-e-Iqbal

Shafique Mor

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

of 123

Appendix M: Speed Observations for University Road

of 123

S no

:Se

ctio

n ID

St

art(

km)

End(

km)

SPE

ED O

BSER

VATI

ON

S (K

m/h

r)

Seg

m(3

-2)

Seg

m(3

-3)

Seg

m(3

-4)

Seg

m(4

-1)

Seg

m(4

-2)

Seg

m(4

-3)

Seg

m(4

-4)

Seg

m(5

-1)

Seg

m(5

-2)

Seg

m(5

-3)

Seg

m(5

-4)

1Se

ctio

n#01

(A-B

)0.

000

0.50

048

5050

5252

5253

5355

5557

2Se

ctio

n#02

(B-C

)0.

500

1.00

059

5656

5758

6061

6161

5958

3Se

ctio

n#03

(C-D

)1.

000

1.50

058

5352

5048

5052

4843

4345

4Se

ctio

n#04

(D-E

)1.

500

2.00

040

4446

4544

4342

4038

3941

5Se

ctio

n#05

(E-F

)2.

000

2.50

056

5656

5657

5656

5757

5858

6Se

ctio

n#06

(F-G

)2.

500

3.00

059

5854

5453

5352

5658

5958

7Se

ctio

n#07

(G-H

)3.

000

3.50

066

6767

6458

5249

4642

4244

8Se

ctio

n#08

(H-I)

3.50

04.

000

6465

6667

6866

6460

5758

58

9Se

ctio

n#09

(I-J

)4.

000

4.50

032

3639

4444

4446

4646

4444

10Se

ctio

n#10

(J-K

)4.

500

5.00

042

4042

4543

3730

2636

4042

11Se

ctio

n#11

(K-L

)5.

000

5.50

020

2622

3026

2932

3233

3234

12Se

ctio

n#12

(L-M

)5.

500

6.00

030

2222

3036

3024

240

55

13Se

ctio

n#13

(M-N

)6.

000

6.50

032

3227

2434

4044

4846

4336

S n

o :

Sect

ion

ID

Star

t(km

)En

d(k

m)

SP

EED

OB

SER

VA

TIO

NS (

Km

/hr)

Seg

m(1

-1)

Seg

m(1

-2)

Seg

m(1

-3)

Seg

m(1

-4)

Seg

m(2

-1)

Seg

m(2

-2)

Seg

m(2

-3)

Seg

m(2

-4)

Seg

m(3

-1)

1Se

ctio

n#0

1 (A

-B)

0.00

00.

500

3236

3940

4142

4445

46

2Se

ctio

n#0

2 (B

-C)

0.50

01.

000

5859

6163

6462

6366

62

3Se

ctio

n#0

3 (C

-D)

1.00

01.

500

5756

5555

5453

5354

56

4Se

ctio

n#0

4 (D

-E)

1.50

02.

000

4832

3741

4438

3840

40

5Se

ctio

n#0

5 (E

-F)

2.00

02.

500

4447

4950

5152

5454

56

6Se

ctio

n#0

6 (F

-G)

2.50

03.

000

5856

5452

5353

5658

58

7Se

ctio

n#0

7 (G

-H)

3.00

03.

500

5959

6060

6263

6465

66

8Se

ctio

n#0

8 (H

-I)

3.50

04.

000

4649

5153

5658

6061

62

9Se

ctio

n#0

9 (I

-J)

4.00

04.

500

6060

6054

4844

4238

28

10Se

ctio

n#1

0 (J

-K)

4.50

05.

000

4644

3832

2830

3438

42

11Se

ctio

n#1

1 (K

-L)

5.00

05.

500

4239

3633

3437

3424

0

12Se

ctio

n#1

2 (L

-M)

5.50

06.

000

3334

3234

3941

4340

38

13Se

ctio

n#1

3 (M

-N)

6.00

06.

500

05

2634

3130

3231

28

of 123

S no

:Se

ctio

n ID

St

art(

km)

End(

km)

SPE

ED O

BSER

VA

TIO

NS

(Km

/hr)

Seg

m(3

-2)

Seg

m(3

-3)

Seg

m(3

-4)

Seg

m(4

-1)

Seg

m(4

-2)

Seg

m(4

-3)

Seg

m(4

-4)

Seg

m(5

-1)

Seg

m(5

-2)

Seg

m(5

-3)

Seg

m(5

-4)

1Se

ctio

n#13

(N-M

)6.

500

6.00

042

4446

4438

4040

3030

3237

2Se

ctio

n#12

(M-L

)6.

000

5.50

031

3330

3433

2926

108

2227

3Se

ctio

n#11

(L-K

)5.

500

5.00

040

4038

3840

4242

4243

4230

4Se

ctio

n#10

(K-J

)5.

000

4.50

031

3025

2122

2430

3232

2830

5Se

ctio

n#09

(J-I

)4.

500

4.00

05

328

3438

3840

4445

4343

6Se

ctio

n#08

(I-H

)4.

000

3.50

048

4950

5052

5354

5555

4837

7Se

ctio

n#07

(H-G

)3.

500

3.00

046

4850

5355

5657

5960

6163

8Se

ctio

n#06

(G-F

)3.

000

2.50

052

5255

5658

6061

6160

5754

9Se

ctio

n#05

(F-E

)2.

500

2.00

056

5860

6160

5655

5658

5860

10Se

ctio

n#04

(E-D

)2.

000

1.50

060

5852

5052

5555

5353

5555

11Se

ctio

n#03

(D-C

)1.

500

1.00

061

5754

5456

5759

6058

5860

12Se

ctio

n#02

(C-B

)1.

000

0.50

072

7374

7270

6662

6161

6364

13Se

ctio

n#01

(B-A

)0.

500

0.00

063

6566

6870

7070

6868

6867

S n

o :

Sect

ion

ID

Star

t(km

)En

d(k

m)

SP

EED

OB

SER

VA

TIO

NS

(K

m/h

r)

Seg

m(1

-1)

Seg

m(1

-2)

Seg

m(1

-3)

Seg

m(1

-4)

Seg

m(2

-1)

Seg

m(2

-2)

Seg

m(2

-3)

Seg

m(2

-4)

Seg

m(3

-1)

1Se

ctio

n#1

3 (N

-M)

6.50

06.

000

510

2028

3232

3740

40

2Se

ctio

n#1

2 (M

-L)

6.00

05.

500

3735

3840

3834

3231

29

3Se

ctio

n#1

1 (L

-K)

5.50

05.

000

3031

3030

3330

3133

38

4Se

ctio

n#1

0 (K

-J)

5.00

04.

500

3030

2626

2828

2329

30

5Se

ctio

n#0

9 (J

-I)

4.50

04.

000

3640

4441

4042

4340

26

6Se

ctio

n#0

8 (I

-H)

4.00

03.

500

4347

4746

4544

4546

48

7Se

ctio

n#0

7 (H

-G)

3.50

03.

000

3024

010

2328

3236

40

8Se

ctio

n#0

6 (G

-F)

3.00

02.

500

6261

6060

5852

5051

52

9Se

ctio

n#0

5 (F

-E)

2.50

02.

000

5448

4642

4246

4951

54

10Se

ctio

n#0

4 (E

-D)

2.00

01.

500

6061

6263

6361

6161

61

11Se

ctio

n#0

3 (D

-C)

1.50

01.

000

5758

5961

6264

6566

65

12Se

ctio

n#0

2 (C

-B)

1.00

00.

500

6264

6567

6969

7070

71

13Se

ctio

n#0

1 (B

-A)

0.50

00.

000

6566

6664

6358

5859

60

of 123

Appendix N: Financial Statement

of 123

ned university of engineering and technology

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