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NED UNIVERSITY OF ENGINEERING AND TECHNOLOGY, KARACHI-75270 DEPARTMENT OF URBAN AND INFRASTRUCTURE ENGINEERING
Chairman/PI Prof. Dr. Mir Shabbar Ali
Professor (Transportation Engineering) Phone: (92-21) 9261261-8 Ext 2354 Fax: (92-21) 9261255 Email: [email protected] [email protected] http://www.neduet.edu.pk/UE/index.htm
Dated: July 4th , 2016
Ms. Afifa Irshad Dy. Director, NRPU(R&D) Higher Education Commission H-9 Islamabad, Pakistan Email: [email protected] Subject: FIRST YEAR PROGRESS REPORT
National Research Program for Universities (NRPU) RESEARCH PROJECT Prediction of Traffic Congestion in Karachi Metropolis using Artificial Intelligence Techniques
Please find enclosed FIRST YEAR report submitted for National Research Program for Universities (NRPU)
RESEARCH PROJECT Prediction of Traffic Congestion in Karachi Metropolis using Artificial Intelligence
Techniques (title page 2).
The duration of the project is of two years, w.e.f. 1st May 2015. HEC allocated total of Rs. 3,703,000 for this
two years research project, out of which Rs. 2,139,000 is already received as Year 1 layout (please see page
3), and being utilized within their respective heads, while Rs. 1,564,000 is to be disbursed in the second year
of research.
A separate account is being maintained by DF-NEDUET and all disbursements are carried out with the
approvals of VC under advice from Resident Auditor, NEDUET. This channel ensures all fund utilization to
be within HEC earmarked heads as well as following SPPRA rules and regulations. The major heads of fund
utilization are provided on page 4.
Submitted for your perusal and necessary action at your end and requested for release of second trench of Rs.
1,564,000 to enable completion of the research activities.
Prof. Dr Mir Shabbar Ali
Enclosures: a) Original Budget Utilization Report
Copy to:
1. Dean (CEA) 2) DF
Page ii of 123
Page iii of 123
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
Page 1 of 123
SECTION 1. EXECUTIVE SUMMARY
1.1. Introduction Our research project, titled “Prediction of Traffic Congestion in Karachi Metropolis through
Artificial Intelligence Techniques”, is intended to fill the void in existing research based on traffic
prediction, with a focus on Karachi‟s indigenous traffic conditions. Congestion studies attribute the
causes of highway congestion to factors known as triggers and drivers, many of which are qualitative
in nature. Computer models that have so far attempted to predict traffic congestion have not been
able to accurately represent these qualitative factors, as a result of which the prediction is inaccurate
and unreliable. Our research utilizes Artificial Intelligence (AI), a branch of computing that is
especially designed to mimic real life and perform calculations on imprecise and non-discrete
phenomena. We are therefore able to factor in some of the most direct qualitative causes of
congestion, such as abrupt lane changing and aggressive driving, in our model, giving it a much
higher degree of accuracy.
Using an expert system to identify and prioritize congestion causes, we will then proceed to gather
information on these causes in real-time conditions. Our research will incorporate visual observation
of traffic streams for information on congestion triggers and drivers prevalent in a selected roadway
stretch. This will be accomplished by CCTV cameras and auxiliary equipment such as digital video
recorders. We will also carry out traffic studies near areas of pavement damage, since these are
important congestion drivers. After studying the impact of specific types of pavement damage and
other observable factors through spot speed and flow measurements, we will use the Fuzzy Logic
Toolbox in MATLAB R2009a to formulate a congestion prediction model. Using a series of if-then
rules, we will be able to predict congestion severity and location on the basis of inputs that are both
qualitative (such as pavement condition) and quantitative (such as traffic flow). Through comparison
with a Multiple Linear Regression model, we will obtain the statistical accuracy of our model.
According to an earlier research project titled „Quantification of Traffic Congestion Cost‟
(conducted through collaboration with NED University‟s Urban Engineering department and Indus
Motors Pvt. Ltd.), the total cost due to congestion in Karachi is approximately Rs. 131.7 million
($1.34 million) per day. This was calculated through determining the amount of time lost by each
person stuck in traffic jams, and multiplying it by the time value of money (dollars or rupees per
hour) for each person. Congestion also has myriad negative effects on the environment, health and
aesthetics of a city. By predicting the location and intensity of a traffic jam, timely efforts may be
made to reallocate traffic to alternative routes so that exacerbation of the congestion may be averted.
Page 2 of 123
This document is divided into 4 sections. Section 1 is a summary of our project and how it will be
undertaken. Section 2 is the progress report, detailing what has been accomplished so far. Section 3
describes transportation research projects that are taking place side by side with this project in our
department. Section 4 is a list of appendices that contain data collection forms (pro formas) the data
we have gathered over the course of the project.
1.2. Background Pakistan is a developing country and many developing projects like shopping malls, commercial and
residential towers (+25 stories) have either been completed recently or under construction in its
cities. Due to these rapid construction activities, the traffic network needs improvement. Road traffic
congestion is a critical problem accelerated by an exponential increase in the number of vehicles and
a high level of urbanization. Optimal utilization of the existing infrastructure can effectively reduce
the congestion levels without the necessity of constructing newer infrastructure to accommodate the
increased traffic volume (Srinivasan et al., 2006).
Karachi is the largest city of Pakistan, having a population of approximately 20,000,000. It is the
economic hub of the country with an international airport named “Jinnah International Airport” and
two sea ports named “Karachi Port” and “Port Bin Qasim”. It has a complex traffic network which
connects commercial and residential zones of the city, which cover an area of 3527 km2. The total
road network in Pakistan was measured to be 258,350 km in 2009. According to the Asian
Development Bank, the number of private motor vehicles in Karachi is growing by 9% per year, and
this adds 280 vehicles every day, leading to immense traffic congestion and causing time loss,
economic loss and health hazards. Time loss includes the delay in travel time, while increasing fuel
usage and vehicle maintenance costs hit citizens economically. Furthermore, air pollution and noise
pollution cause health hazards. These factors negatively affect the country‟s economy and the
lifestyle of its citizens. Therefore, it is necessary to have a traffic congestion model to predict travel
time delay, reflecting the influencing factors in a particular link for controlling and managing the
traffic in an efficient way.
It was found that traffic congestion cost of Karachi in 2013 is 688 million USD per year and it is 2%
of the total revenue of Pakistan. For an urban city of developing countries, traffic congestion cost
may be around 1-2% of the GDP that particular city is contributing (M.S. Ali et al., 2013).
Moreover, the urban areas of Karachi experience more traffic volumes as compared to industrial
areas. From the time based volumes of an urban highway in Karachi, we can see that the peaks in
both personal travel and transport of goods occur between 9 a.m. till 7 p.m.
Page 3 of 123
0 5000 10000 15000 20000 25000
7:009:00
11:0013:0015:0017:0019:0021:00
Quaidabad to Mazil Pump
Total Persons
0 5000 10000 15000 20000 25000
7:00
9:00
11:00
13:00
15:00
17:00
19:00
21:00
Mazil Pump to Fast Uni
Total Persons
0 5000 10000 15000 20000 25000
7:00
9:00
11:00
13:00
15:00
17:00
19:00
21:00
Fast Uni to Port Qasim
Total Persons
0 2000 4000 6000 8000 10000 12000
7:009:00
11:0013:0015:0017:0019:0021:00
Port Qasim to Pakistan Steel
Total Persons
Figure 1.2.1: A time-based comparison of four stretches of Shahra-
e-Faisal with respect to total persons traveling.
Page 4 of 123
Fig 1.2.2: A time-based comparison of four stretches of Shahra-e-
Faisal with respect to total persons traveling.
Page 5 of 123
1.3. Current State of Research
Although several congestion models have been developed (Lindsey et al. 1999), the overwhelming
majority of them have focused on the quantitative causes of congestion. It is well known that several
qualitative factors such as driver behavior, ease in buying vehicles, pavement condition and vehicle
heterogeneity are either triggers or drivers for congestion. By excluding these from a congestion
model, the accuracy and applicability of the model suffers. Although a multivariable approach may
be used to bring some of these factors close to a discrete value, this is both time-consuming and
difficult to reproduce for models in different regions.
Secondly, artificial intelligence has not been used for calculations in these models. The advantage of
using AI is that it can process quantitative and qualitative factors more accurately and can also learn
calculation processes (such as through neural networks). This allows the model to be iteratively
improved until a desired level of accuracy is achieved. This is ideal for a congestion model, where
input values can change rapidly and unexpected trends in traffic behavior are common (such as
during periods of inclement weather, rallies or public gatherings, or VIP movement). By using AI
techniques, our model can be comprehensive, incorporate many inputs while allowing the easy
addition of new variables, and can be quickly adapted for new regions.
1.4. Scope and Objectives Our primary objective is to develop a comprehensive congestion prediction model for urban
networks that successfully incorporates qualitative and quantitative congestion causes and accurately
simulates their effects through fuzzy logic. This will remedy the main shortcoming of existing
models, namely, their failure to accurately capture the effect of qualitative congestion causes and
perform accurate calculations on dynamic inputs. Although we have chosen the road network of
Karachi as our research area, we anticipate that our model will be equally accurate when applied to
urban networks of similar magnitude and complexity.
Dissemination of the output of this project will be carried out through Karachi Metropolitan
Corporation (KMC), Transport Planners/ Traffic Engineering consultants. The output of the project
will be to determine and predict the ideal free flow speed without delay with minimum influences of
adverse qualitative and quantitative variables. The project will identify the most critical variable
(qualitative or quantitative) which results in maximum traffic congestion and suggested to be
improved. For example, if at specific corridor pavement condition is highlighted (through modeling)
as most severe variable causing delay in the traffic, and with the maintenance of pavement condition
the severe traffic congestion can be reduced for that specific corridor. The relevant department of
KMC will be approached and advised to improve the pavement conditions.
Page 6 of 123
1.5. Methodology Phase I of this research includes an extensive literature review through which use of Artificial
Intelligence techniques in the field of transportation engineering, especially for prediction purposes
are explored. In our case, the Fuzzy Logic toolbox of MATLAB (R2009a) is selected to develop a
prediction model. This tool creates input space to an output space through a mechanism of if-then
rules. For the development of model, it is very necessary to understand the factors on which traffic
congestion depend and how will it be used in fuzzy logic to achieve a model of the desired accuracy.
For this purpose the literature review is divided into two parts; part one is focused on obtaining the
qualitative and quantitative factors with their impact on traffic congestion, and part two is focused on
software exploration: how MATLAB works using fuzzy logic tool to understand the mechanisms
and the theory behind the fuzzy logic tool.
The qualitative and quantitative factors that affect traffic congestion are identified and prioritized to
assess their impact on traffic congestion.
Quantitative Factors
i. Lane width
ii. No. of lanes
iii. Traffic composition
iv. Population growth
v. Travel speed
vi. Traffic volume
vii Road capacity
Qualitative Factors
i. Pavement condition
ii. Type of land use (residential, commercial, industrial)
iii. Bus stop availability
iv. Weather condition (rainfall)
v. Driving behavior (tolerance level/aggression level)
Page 7 of 123
vi. Presence of road intersection at small intervals (approximately 0.5 km)
vii. On-street parking
In quantitative parameters, lane width and no. of lanes reflect road capacity. Travel time, traffic
volume and traffic composition reflect traffic characteristics. Population growth rate incorporates
future usage of road intersection.
In qualitative parameters, psychological factors include driving behavior, while land use determines
the level of interruption in traffic flow due to on-street parking and presence of hawkers along road
sides. Road surface condition and presence of road intersections reflects planning and regulation
work.
1.6. Summary of progress to date We have completed our literature review on congestion and its causes, artificial intelligence and
Karachi‟s main arterials. We conducted an expert opinion survey for identifying and prioritizing
causes of congestion (Appendix A), and divided the identified causes into static and dynamic factors.
We then selected arterials in Karachi for our study – University Road, Shahra-e-Faisal, Sher Shah
Suri Road and Nawab Ali Siddique Khan Road, Shahra-e-Pakistan, Jamshed Road and M. A. Jinnah
Road, and Rashid Minhas Road (Appendix B and C). We conducted a pilot survey of the congestion
levels on University Road through floating car technique and collected speed and pavement
condition data (Appendix M), and collected traffic videos for one whole day on three locations on
Rashid Minhas Road. We later used these videos for a pilot study on driver behavior and vehicle
counting (Appendix K and L). Using Google Maps, we identified areas and times during which
congestion will be highest on the selected arterials (Appendix D). We also collected encroachment
data and pavement condition data for the identified sections on Rashid Minhas Road and University
Road (Appendix H). Using Google Earth, we made a land use map for the selected arterials
(Appendix J).
1.7. Anticipated deliverables and time line This research will yield valuable data on the arterials we have selected. We already have a
congestion map showing the times and locations of congestion on these arterials. We plan to collect
complete data on pavement condition, encroachment, bottlenecks and the other identified factors for
these arterials. Other than the congestion model, we will also have collected data on driver behavior,
vehicular mix and traffic flows upon the completion of our project, which will be in July 2017.
Page 8 of 123
1.8. Collaborations established In line with the research proposal submitted and accepted by HEC, this research is enabling
stakeholders to benefit from the expertise and vision of the research team and the research outputs to
date.
One of a remarkable illustration is the utilization of real time traffic updates map by DIG traffic in
their command and control centre established in Karachi, the idea of which was shared by the
research team. Secondly, a WhatsApp group has been established to provide live traffic updates by
DIG traffic office in which our research team member provides active inputs. Thirdly, this research
is benefited directly from various traffic posts established in Karachi, in the form of their inputs in
identification and confirmation of traffic congestion locations.
1.9. Research outputs In terms of research outputs, the first year of the research has been able to produce three research
paper drafts, one final year project and has identified five masters research projects which will be
started in the fall 2016 semester at NED University.
Page 9 of 123
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Page 10 of 123
SECTION 2. RESEARCH PROGRESS
2.1. Literature Review
2.1.1. Traffic congestion as a major civic problem
Traffic congestion is variable in its description, since it is closely linked to the Level of Service,
which itself depends on diverse user opinions. For the purpose of simplicity, it can be defined in
three different ways.
i. The more complete definition of excessive congestion is “when the marginal costs of congestion to
society exceed the marginal costs of efforts to reduce congestion.”1 In other words, when the cost
of congestion (due to wastage of time and pollution due to idling vehicles) is higher than the cost
of widening roads and implementing other congestion-reducing measures, congestion can be
termed “excessive”.
ii. Congestion can be said to arise when the general flow of a roadway exceeds its dynamic capacity.
The dynamic capacity is set by the interaction of vehicle types and lengths, traffic speeds, ingress
and egress patterns, lane switching and car following behavior, and is influenced by the
atmospheric and road conditions. The variable nature of dynamic capacity makes it a much more
realistic and useful descriptor of roadway capacity, since studies have consistently shown that
roadway capacities become unpredictable as traffic flows change from “decreasing speed,
increasing flow” to “decreasing speed, decreasing flow”2. This occurs at the apex of the curve
shown below.
Fig. 2.1.1.1: Speed Flow Curve for Uninterrupted Highways3
iii. A shorter and more practicable description of congestion is “when the throughput of a roadway is
decreasing despite decreasing vehicular speed.” Roads are designed to serve a maximum number
of users, and as this number increases, the average speed at which users traverse the facility is
sacrificed. However, when the speed as well as the throughput suffers due to the traffic level, and
the economic benefit of building the facility is reduced, the facility can be considered “congested.”
Page 11 of 123
Traffic congestion is a problem that has plagued developed and developing countries alike, which
often leads to the perception that it is an unavoidable outcome of a growing population and
economy. Increasing the capacity of roads and mass transit often provides only a temporary solution:
despite an extensive network and high ridership in New York‟s subway system, rush hour
congestion in the city is very high. There is no doubt, however, that decongestion measures such as
car parks, mass transit and pedestrian/cyclist friendly cityscapes help take several vehicles off the
road. Example
Congestion harms the environment in numerous ways. Vehicles waste fuel while idling, thereby
contributing to global warming and depletion of fossil fuels. Vehicular emissions cause acid rain,
smog, discoloration of urban structures and several diseases in humans such as respiratory problems,
cancer, and stress. Although it is often argued that a lowered speed due to congestion reduces the
severity of accidents, it has often been observed that drivers speed up after escaping from a
congestion hotspot, which increases the risk of accidents. Roads also deteriorate prematurely, since
they are not designed to accommodate extremely slow moving vehicles. Vehicles also contribute the
heat island effect, especially while they are stuck in a traffic jam.
Congestion is a direct outcome of not just urban sprawl but also the ideal of a car and a wide road
close to one‟s residence. By designing a city in such a way that motorized vehicles become
indispensable to transport, congestion and pollution become inevitable. Any congestion mitigation
strategies that free up road space temporarily will soon be overwhelmed by induced demand. While
it is commonly agreed upon that it is virtually impossible to significantly and permanently reduce
congestion, planning the city in a way that reduces the dependency on private vehicles is the most
important requirement for preventing congestion.
Sources:
1. Adapted from VCEC (2006), p. xvi.
2. http://www.internationaltransportforum.org/pub/pdf/07congestion.pdf
3. ECMT (2007)
Page 12 of 123
2.1.2. Previous studies on traffic congestion issues
One of the most comprehensive documents on traffic congestion is a report titled „Managing Urban
Traffic Congestion‟, published by the Transport Research Centre. This is a joint project of two
international organizations, the Organization for Economic Co-Operation and Development and the
European Conference of Ministers of Transport. Among the topics covered in this report are
1. Defining congestion
2. Causes of congestion
3. Assessment and measurement of congestion
4. Congestion response and mitigation strategies
This report, and the research papers referenced therein, described the following patterns,
observations and ramifications of congestion:
1. Traffic congestion is an inevitable outcome of economic and population growth
2. Because roads are not designed to be used at free-flow speeds, it is erroneous to assume that time
is wasted because of reductions in speed
3. It is impossible to significantly and permanently reduce congestion
4. Congestion on interrupted and uninterrupted links is caused by different factors
5. Induced demand means that any reductions in congestion are temporary
6. It is necessary to bring congestion down to a manageable level to avoid extreme environmental
degradation. Some mitigation strategies include car parks, mass transit and congestion pricing.
2.1.3. Recovering from congestion:
Congestion and subsequent recovery is known to follow hysteretic behavior. This means that the
relationship between the cause and effect is such that reversing the cause by a certain amount does
not reverse the effect by the same amount. When the flow of traffic breaks down from B to D as
shown in the figure above, the change is sudden and temporary. In order for the flow to recover, the
traffic density must be lowered significantly and not just to the density at point B. This much lower
density will allow the vehicles to accelerate fast enough away from the congested area so that
recovery can begin. Therefore, a failure to provide this low density can prolong existing congestion.
The figure below illustrates hysteretic loading and recovery.
Page 13 of 123
Fig. 2.1.3.1: 2- and 3-Phase Flow-Density Diagrams (Adapted from Maerivoet, S. and
de Moor, B. (2006)
2.1.4. Causes of Congestion
The causes of traffic congestion can be categorized as triggers, drivers and random factors.
Triggers are micro-level actions that are the most immediate and direct cause of congestion.
Examples include bottlenecks, sudden changing of lanes or rapid deceleration. Triggers can be
readily identified or measured.
Drivers are macro-level conditions that originate from the demand for transportation. Examples
include increasing population, car ownership and dependency, and availability/cost of parking.
Drivers contribute to the incidence of congestion and its severity. They also include exogenous
factors such as second-order demand and trip patterns and volumes.
Random factors are those related to largely unforeseen events such as weather and poor visibility.
They are not very important since there are ways to account for their effects while planning for
congestion, based on the likelihood of their occurrence and severity. That is not to say that their
effects are not important8, rather, they can be accounted for even though they inherently unplanned.
How does congestion occur in uninterrupted links?
Congestion occurs due to a convergence of circumstances. The same triggers that bring about the
congestion may have been occurring before in free flow conditions without causing congestion.
Similarly, even when the roadway demand has equaled or exceeded its capacity, congestion may still
Page 14 of 123
be avoided; however, it is also possible for congestion to occur before the demand equals the
capacity (for example, due to a vehicular collision).
Traffic congestion utilizes the concept of dynamic capacity rather than traditional concepts of fixed
capacity. Now, as demand changes, so can the capacity of a roadway. As a result, the relationship
between demand and capacity becomes probabilistic rather than deterministic.
To simplify, we can say that congestion occurs when incidents such as lane changing, following
distance fluctuation or vehicular collisions result in a transition from decreasing speed and
increasing throughput, to decreasing speed and decreasing throughput.
Congestion can be recurrent or non-recurrent. Recurrent congestion can be due to rush hour traffic or
weekend trips, and is clearly the less worrisome of the two, since travellers can adapt to it and
change their trips accordingly. Non-recurrent congestion can be due to aberrant weather or road
works, and accounts for around 55% of all congestion3. However, by planning for these delays
through congestion management policies, this can be brought down to 14 – 25%4.
Triggers on uninterrupted links:
The following are known to cause sudden, temporary changes in throughput capacity of an
uninterrupted roadway:
o Car following behavior (distance and gap choices)
o Speed choice and differential speeds
o Acceleration and/or deceleration
o Lane-changing behavior
The moment at which congestion will be triggered can be determined by the sequence and mix of4:
Vehicle types
Driver types (risk prone, risk averse, aggressive)
Information level of drivers (familiarity with route, congestion expectancy etc.)
Trip purposes
Driver moods
There are 4 major types of bottlenecks on roadways5:
1. Visual effects for drivers, such as
a. Roadside distractions
b. Rubbernecking
Page 15 of 123
2. Abrupt changes in highway alignment, such as
a. Sharp curves
b. Hills
c. Work zones
3. Intended interruptions to flow, such as
a. Signals
b. Tollbooths
4. Vehicle merging maneuvers, such as
a. Lane drop (lane is lost) at bridge crossings and work zones
b. Crashes and debris
c. Vehicles having to weave through traffic to enter and exit
d. Freeway interchanges/ramps
e. Micro-bottlenecks due to lane changing and speed differentials
Congestion triggers on interrupted-flow facilities:
While motorways and other signal-free corridors have their congestion defined using flow and
dynamic capacity, on urban roads with signalized intersections congestion can simply be quantified
as delay. Hence, anything that delays the movement of traffic on these roads acts as a trigger.
In urban roads, the link capacity is less important in determining congestion than intersection
capacity, even though it may be much more than the latter. The intersection capacity depends on the
physical and operating characteristics of the incoming and outgoing links, as well as the geometric
design of the intersection (such as left-turn lanes for left-handed cars) and on-street parking
configuration at or near the intersection.
At the intersection itself, the driver behavior is affected by:
a. Built environment
b. Signage
c. View sheds
d. Geometric disposition of intersection
The most important trigger by far is the traffic signal itself. Poorly coordinated traffic signals are the
biggest cause of urban congestion. Other important factors that can cause delays include:
Parking maneuvers
Delivery traffic such as bus stops
Page 16 of 123
Turning movements
On the unsignalized intersections between major and minor streets, queues can form which lead to
delay. Gap sizes between vehicles depend on the types of maneuvers (left, right, through), number of
lanes, and the speed of major street vehicles and sight distances.
Congestion drivers:
The demand for transportation is the basic cause for all congestion. This demand can come from a
number of factors:
1. Social and economic growth.
2. Increasing population
3. Car ownership and dependency
4. Land uses
5. Travel patterns
6. Public transport options
7. Urban freight transport and goods delivery
8. Parking
A study of these factors in the milieu of congestion illustrates that vehicles and the congestion that
they cause are not just influenced by the urban environment, but they also shape the urban character
around them. The relationship is bidirectional. For example, one reason why people buy cars could
be that activity centers are spaced too far apart. Eventually, future activity centers will be spaced
apart to avoid the congestion resulting from the influx of new cars, while roads will become noisy
and the environment will become polluted. Just as the decision to buy a car was influenced by socio-
economic factors, the environment and the urban layout, buying a car and using it will affect the
same factors as well.
The main stimulants for car ownership can therefore be identified as:
a. Population increase
b. Personal income growth
c. Workforce habits or requirements (if telecommuting jobs are replaced with those
requiring frequent travel, such as that of salesmen, congestion will occur)
d. Complex urban mobility patterns
e. Urban growth in suburbs (small roads not being equipped to handle traffic)
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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
Page 28 of 123
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
Page 29 of 123
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
Page 31 of 123
Fig. 2.5.1.2: Video Recording at Gulshan-e-Iqbal
Fig. 2.5.1.3: Video Recording at Shafique Mor
Data extracted from video at pedestrian bridge near Aladin park
Analysis of driver behavior
Scores were assigned to different lane-changes based on how much they impacted the
rest of the traffic platoon. A score of 2 was assigned to a vehicle every time it crossed
the lane marker between the fast and center lane. However, if the vehicle crossed the
marker between the slow lane and center lane, it was assigned a score of 1, since too
few vehicles are usually using the slow lane for the platoon to be disrupted. If it was
being driven over a lane marker, it was assigned a score of 1 (detailed in Appendix K)
Page 32 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
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
Page 48 of 123
The unit of measurement of these influencing factors and travel speed is also analyzed
and identified as shown in Table 2.6.2.3:
Unit Description
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
Page 52 of 123
within their respective heads, while Rs. 1,564,000 is to be disbursed in the second year of
research.
A separate account is being maintained by DF-NEDUET and all disbursements are carried
out with the approvals of VC under advice from Resident Auditor, NEDUET. This channel
ensures all fund utilization to be within HEC earmarked heads as well as following SPPRA
rules and regulations. The major heads of fund utilization are as follows:
2.8.1. Research staff
Dedicated research staff has been appointed within the budget allocated, in order to facilitate
smooth running of the project.
Designation Name Qualification
Research Assistant S. M. Raza Jafri B.E. (Urban Engineering, NEDUET)
Research Support Staff Taimoor Hassan Babar B.S. (Civil Engineering, BUITEMS)
Research Support Staff Aakefa Qaiser B.E. (Urban Engineering, NEDUET)
2.8.2 Equipment
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
Page 53 of 123
6. Institutional Overhead
7. Local Travel
8. Miscellaneous
2.8.4 Publications
Under the procurement head of “publications”, international state-of-the-art books are
procured. Details are given below:
S. No. Title / Edn. / Vol. / Year Author Publisher ISBN/ ISSN Price (Rs.)
(Please Use Capital Letters) (1st & Last Name)
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
Page 54 of 123
SECTION 3. AUXILIARY RESEARCH PROJECTS
This project is running concurrently with a few other research projects, allowing us to share
data and streamline our efforts.
3.1. Correlation between Driver Behavior and Traffic Heterogeneity
We see whether certain types of driver behavior (such as lane changing and sudden braking)
are affected by the traffic heterogeneity (a measure of how diverse the vehicles are in the
traffic stream for a given time period).
Overview
Heterogeneity of traffic is known to affect various traffic parameters such as speed, headway
and flow. Intuition suggests that the heterogeneity may also affect driver behavior, similar to
the findings of „shared space‟ experiments. These experiments found that confusing the
drivers by removing road signage and demarcation structures caused them to slow down,
resulting in improved safety. By corollary, driver behavior may be more sensitive to a diverse
mix of vehicles as opposed to a homogenous, „expected‟ mix. In a heterogeneous mix of
traffic, drivers may be unsure of how much headway to maintain with respect to the different
vehicles, and more overtaking or lane changing may occur due to the differences in speeds
and accelerations between the different vehicles. The resulting visual distractions (due to
unexpected vehicle types appearing in the driver‟s field of vision) and cognitive distractions
(from thinking about how much headway to maintain) are two of the four kinds of
distractions known to affect drivers (Stutts et al., 2005).
As the diversity of the mix increases, a sense of inequality may arise in some road users,
leading to a competition often based on the size/quality of the competitors‟ vehicles (Novaco,
1989). Road rage, excessive signaling or honking, and overly aggressive or conservative
driving may therefore be some of the associated effects of traffic heterogeneity.
Of course, driver behavior is also influenced by factors like traffic volume, pavement
condition and local knowledge of roads (for example, slowing down before reaching an area
notorious for its jaywalkers). Any attempts to correlate driving behavior and traffic
heterogeneity should consider locations and timings where the other factors are minimized. A
stretch of road with satisfactory pavement condition, fairly uniform traffic volumes (with few
breakdowns in flow) and no aberrant conditions (such as wrong-way movement, jaywalking
and encroachment) will yield ideal data for this correlation.
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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
Page 59 of 123
through traffic in that it interrupts the through traffic movement. There are a number of
factors that may be concerned for capacity analysis of U-turns at signal free corridors as such
its effect on the capacity of road, as the U-turn vehicles wait for a large enough gap before
making the manoeuvre. There are interactions between through traffic and U-turns traffic
streams. When the through traffic volume increases, it lessens the chances for the U-turns
traffic to move. This is of major concern that whether it is useful to allow U-turns to be made
in future considering the current situation at signal free corridors, or it is better to use of
signalized intersection. The main focus of this report is to analyse the capacity of the traffic
flow that uses U-turns and investigate whether it is a convenient method of using u turns or
should there be alternatives to be used in the future to solve the problems of traffic congestion
in metropolitan cities such as Karachi. Apart from using U-turns there is another alternative
that is used in Karachi as well as other big cities around the world: making a signalized
intersection where traffic has to wait for designed time period at signal that also has impact
on the capacity of the road.
Objectives
The major objective of the project is to form a probabilistic methodology to analysis the
conflict points, traffic jams and traffic congestion due to U-turns at Signal Free Corridor.
• Capacity Analysis of U-turns at Signal Free Corridor.
• Proposal of Signalize intersection.
• Comparison of proposed signalizes intersection with existing U-turns.
Scope & Limitation
This project has a vast scope in solving our current situation of the traffic congestion due to
U-turns at signal free corridors and in establishing a research that is focused on the
operational performance of U-turning to straight movement. The operational effects of U-
turning heavy vehicles would not be considered. This research analysis the different possible
outcomes of using U-turns that affects the road capacity, and to provide a suitable possible
substitute that can increase the road capacity in negligence to traffic jams and congestion in
the roads and to provide a sustainable transportation environment in urban arterials. In
addition, this research is limited to the urban and suburban environments.
Page 60 of 123
Fig. 3.3.1: Number plate data for finding travel times of vehicles between locations
Page 61 of 123
SECTION 4. APPENDICES
Appendix A: Expert Opinion Form for Causes of Traffic Congestion 65
Appendix B: Survey Form for Congestion on Arterials 69
Appendix C: Map of Selected Arterials of Karachi 73
Appendix D: Congestion Chart 76
Appendix E: Plan for Recording Traffic Videos at Selected Locations and Times 82
Appendix F: Pro formas 84
Appendix G: Relative Importance Index for Prioritizing Factors 87
Appendix H: Encroachment and Pavement Condition Data at Selected Locations 89
Appendix I: Number and Width of Lanes of Selected Roads (Static Factors) 95
Appendix J: Land Use (Static Factors) 105
Appendix K: Driver Behavior (Dynamic Factors) 111
Appendix L: Traffic Counts 113
Appendix M: Speed Observations for University Road 116
Appendix N: Financial Statement 119
Page 62 of 123
Appendix A: Expert Opinion Form for Causes of Traffic
Congestion
Page 63 of 123
Page 64 of 123
Page 65 of 123
Page 66 of 123
Appendix B: Survey Form for Congestion on Arterials
Page 67 of 123
Page 68 of 123
Page 69 of 123
Page 70 of 123
Appendix C: Map of Selected Arterials of Karachi
Page 71 of 123
Page 72 of 123
Key: White – Korangi Road
Green – Shahra-e-Faisal
Pink – I. I. Chundrigar Road
Yellow – M. A. Jinnah Road
Orange – Karsaz Road
Red – University Road
Cyan – Shahra-e-Pakistan
Blue – Sher Shah Suri Road
Black – Rashid Minhas Road
Note: Karsaz Road, I. I. Chundrigar Road and Korangi Road were omitted from
our study due to lack of congestion data on Google Maps or insufficient arterial
length. M. A. Jinnah, Jamshed Road and Shahra-e-Pakistan were considered as
one contiguous arterial.
Similarly, Sher Shah Suri Road and Nawab Siddique Ali Khan Road were
studied as one arterial.
Page 73 of 123
Appendix D: Congestion Chart
Page 74 of 123
Page 75 of 123
Page 76 of 123
Page 77 of 123
Page 78 of 123
Page 79 of 123
Appendix E: Plan for Recording Traffic Videos at Selected
Locations and Times
Page 80 of 123
Page 81 of 123
Appendix F: Pro formas
Page 82 of 123
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
Page 83 of 123
Page 84 of 123
Appendix G: Relative Importance Index for Prioritizing
Factors
Page 85 of 123
Page 86 of 123
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
Babar Hospital (Right)
1 2
Jaama Masjid Mujaddid Sani
1 2
Off ramp near Civic Center
3 2
Road junction before pedestrian bridge
2 1
Billboard in front of expo center
3 1
Innovative IT Training institute
4 2
Just before Pedestrian Crossing
3 1
Pizza Crust
3 4
Bank Islami (Right)
4 2
Just before Al Mustafa Medical Center
4 3
Bank Islami
Jail Chowrangi to Bank Al-Islami
Page 87 of 123
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
Page 88 of 123
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
Page 89 of 123
Locations (200m apart) Pavement Conditions Encroachment Comments
Intersection with R.A. Jafri Road (Dayyar e Shereen)
1 2
100m before Café Allah o Akbar
1 2
100m before CNG Station on right
2 2
Hashim Khan Quetta Hotel
3 3
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
Page 90 of 123
Ali Square
1 1
Point CNG (opposite)
1 1
The road just after the ground
opposite Point CNG (+50m)
1 1
Aerosoft World (opposite)
3 1
Road after ending of complex
(left)
4 3
Bank Al-Islami (opposite)
1 1
Al-Prince Market
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
Page 91 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
Locations (200m apart) Pavement Conditions Encroachment Comments
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
Page 92 of 123
Appendix I: Number and Width of Lanes of Selected Roads
(Static Factors)
Page 93 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
Babar Hospital (Right)
3 10.13
PIA Garden
3 12.07
Bank Al-Islami (Left)
3 9.05
Sir Syed University
3 10.77
Lalazar Banquet (Left)
3 10.21
Usman Institute of
Technology
Direction 2:
University RoadUsman Institute of
Technology
3 10.62
Lalazar Banquet (Left)
3 11.58
Sir Syed University
3 10.4
Bank Al-Islami (Left)
3 12.19
PIA Garden
3 10.64
Babar Hospital (Right)
3 10.28
Wildlife Aquarium
4 13.31
Jail Chowrangi
STATIC FACTORS
UNIVERSITY ROAD
Jail Chowrangi to Safoora
Safoora to Jail Chowrangi
Page 94 of 123
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
Hashim Khan Quetta Hotel
3 11.49
Edhi Sard Khana
2 7.06
Fazal Mill
3 11.46
UBL Sports Complex
3 11.07
Shabbir Ahmed Usmani
Flyover
2 7.01
NIPA
3 11.02
Aladin Park
3 11.14
Four Seasons Banquet
3 11.58
Lavish Dine
2 8.01
C.O.D Lawn
3 11.01
C.O.D Flyover
RASHID MINHAS ROAD
Bar B Q & Roll Point to C.O.D. Bridge
Page 95 of 123
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
Hashim Khan Quetta Hotel
3 11.5
Dayyar e Shereen
(intersection with Raees
Ahmed Jafri Road)
2 7.1
Bar B.Q and Roll Point
C.O.D. Flyover to Nagan Chowrangi Flyover
Page 96 of 123
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
Page 97 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
Page 98 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
Page 99 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
Page 100 of 123
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
Page 101 of 123
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
Page 102 of 123
Appendix J: Land Use (Static Factors)
Page 103 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
Jail Chowrangi to Safoora University Road
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
apprt + shops 141.67
1.5 1.5 2 3
Jail Chowrangi to Safoora University Road
PIA Garden - Bank Al-Islami Residential CommercialOpen
spaceInstitutional
Commercial
+
Residential
Recreational
shops resturant 185.3
shops 219.71
open area 109.46
4 1
Jail Chowrangi to Safoora University Road
Sir Syed University - Bank Al-Islami Residential CommercialOpen
spaceInstitutional
Commercial
+
Residential
Recreational
Sir Syed University 250.88
alig instit 229.07
apprt + shops 154.57
flats 195.47
shops+ flats 155.35
shops 133.49
2 1.5 4.5 3
Safoora to Jail Chowrangi University Road
Page 104 of 123
Note: The values represent the covered area of the different types of land uses in square
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
Safoora to Jail Chowrangi University Road
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
Safoora to Jail Chowrangi University Road
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
C.O.D. Flyover to Nagan
Chowrangi Flyover
Rashid Minhas
Road
Page 105 of 123
Note: The values represent the covered area of the different types of land uses in square
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
C.O.D. Flyover to Nagan
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
Nagan Chowrangi Flyover
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
Nagan Chowrangi Flyover
to C.O.D Flyover
Rashid Minhas
Road
Page 106 of 123
Note: The values represent the covered area of the different types of land uses in square
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
Page 107 of 123
Note: The values represent the covered area of the different types of land uses in square
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
Sohrab Goth to Teen Hatti
Bridge
Shahrah-e-
Pakistan
Sohrab Goth to Teen Hatti
Bridge
Shahrah-e-
Pakistan
Page 108 of 123
Appendix K: Driver Behavior (Dynamic Factors)
Page 109 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.
Page 110 of 123
Appendix L: Traffic Counts
Page 111 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
Page 112 of 123
TIMES CARS BUSES/TRUCKS BIKES RICKSHAW/QINCHI Total
10:30 0 0 0 0
10:35 211 23 261 71 566
10:40 214 22 287 73 596
10:45 153 12 231 71 467
10:50 198 11 246 80 535
10:55 185 12 201 50 448
11:00 192 17 260 68 537
11:05 174 21 256 78 529
11:10 214 21 247 64 546
11:15 91 10 148 40 289
11:20 166 19 247 43 475
11:25 173 22 241 72 508
11:30 194 17 290 61 562
11:35 181 15 256 64 516
11:40 217 23 287 75 602
11:45 209 18 310 70 607
11:50 186 23 274 72 555
11:55 179 12 270 48 509
12:00 190 26 277 66 559
12:05 224 12 278 72 586
12:10 225 23 263 62 573
12:15 236 24 338 70 668
12:20 219 19 293 54 585
12:25 258 19 333 60 670
12:30 233 17 273 76 599
12:35 223 22 329 64 638
12:40 234 19 419 80 752
12:45 225 16 342 78 661
12:50 253 15 393 65 726
Aladin Park
Page 113 of 123
Appendix M: Speed Observations for University Road
Page 114 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
Page 115 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
Page 116 of 123
Appendix N: Financial Statement
Page 117 of 123