TRTC Brief Summary Report

8/17/2019 TRTC Brief Summary Report

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Table of Content

1. Research Project Team 1

2. Introduction 2

3. Project Methodology 3

4. ome !eneral Traffic Trends "

#. $uantification of Congestion %e&els 11

'. $uantification of Traffic Congestion Cost 13

". Conclusion 1"

(. )c*no+ledgement 1"


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T-RTC Brief Research Report Page 1

1. Research Project Team

Research Team from ,- /ni&ersity of -ngineering and Technology

1.

Prof. r. Mir habbar )li (Principal Investigator of the T-RTC Project, and

Chairman, Department of r!an and Infrastr"ct"re #ngineering, $#D niversit%&

'. r. Muhammad )dnan (Co-Principal Investigator of the T-RTC Project, and

ssociate Professor, Department of r!an and Infrastr"ct"re #ngineering, $#D

niversit%&

).

Mr. 0afar Ibal (*enior Traffic Cell +anager, T-RTC Project&

. yed aal )bbas aueri (Research ssistant, T-RTC Project&

. yed Muhammad ,oman (Research ssistant, T-RTC Project&

. Mr. Talha ha*eel Mallic* (Traffic Cell +anager, T-RTC Project&

rom Indus Motor Com5any %td 5artici5ated as 6ey ta*eholder

1. Mr. Par&e !hias (Chief #/ec"tive 0fficer, I+C&

'. Mr. abar alim (+anager Planning Research, I+C&

).

Mr. Muhammad )rshad (ssistant +anager 2R C*R, I+C&

7ther ta*eholder 5artici5ated in the Project

Mr. ,ia iddi*i (3ormer DI4, *ind Police&


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T-RTC Brief Research Report Page '

2. Introduction

2.1 Moti&ation

Congestion on roads is a phenomenon that occurred when traffic volume is approaching

capacity of the road section. Travelers and commuters experiencing this phenomenon on

daily basis while commuting for different activities at different times of day. Congestion on

the roads of Karachi is worse than many other cities in the world, and it may get worse year

by year. According to recent JICA study, Traffic growth in Karachi is following an

exponential path. Currently there are 1.3 million vehicles are registered in Karachi (that

almost constitute half of the National total). Private vehicle (mainly cars and motorcycles)

ownership is increasing at annual growth rate of 9% and it has been estimated that around

300 vehicles are added on the road of Karachi every day. This significant increase in vehicle

ownership indicates a marginally better condition of economy, however, on the other hand

road network of Karachi experiencing significant decrease in their performance level because

of traffic delays. Government officials have planned several key road developmental projects

and also CDGK has proposed a mass transit plan till 2030 to improve traffic situation of

Karachi with cooperation of JICA, but nature of traffic growth is such that it is growing faster

than the road capacity. So the occurrence of the phenomenon of congestion will be long

lasting. This situation demands for adopting such sustainable policies and strategies which

are vital to reduce congestion, however, the effectiveness of these policies is entirely

dependent on appropriate quantification of congestion cost. It is therefore vital that as a first

step, to conduct such a program or study through which a value of traffic congestion is

estimated.

Under the circumstances, Indus Motor Company Limited (IMC) took an initiative as a key

stakeholder and commissioned a research study, first time of its kind in Pakistan, engaging

NED University to quantify the economic cost of traffic congestion on selected road of

Karachi. This chapter presents an overall framework of the research methodology to carry out

the different tasks involved in order to achieve relevant objectives of the proposed research.

2.2 tudy 7bjecti&es and 7&erall rame+or*

The key objectives of research study are as follows:

• To ascertain the level of congestion through key traffic parameters of a selected road

of Karachi

• To quantify the cost of congestion as a general estimate incorporating all travelers of

a selected road.


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T-RTC Brief Research Report Page )

Quantification of the congestion cost as a general estimate is useful in a sense that it provides

the measure of economic burden on the road user which travelling on a particular road, and

any improvement through Transport demand management strategies or capacity improvement

can be easily justified based on their cost and benefits. Based on the objectives mentioned

above, this research study is focused on determining an overall congestion cost incurred

collectively by all road users. Important steps that are involved to achieve the objectives of

this research are selection of a stretch of road and collection of traffic volume data on daily

basis for 24hrs. This can be done by installing video cameras at key points and then

processing obtained video clips for extraction of key parameters e.g. free flow speed, traffic

volume, avg. speed of vehicles and avg. travel time. In order to estimate the cost of

congestion, questionnaire based survey exercise was performed to determine how travelers

value their time. Usually an indirect method is used that involve estimation of discrete choice

model for mode choice, this model contain parameters such as travel cost and travel time and

ratio of their co-efficient is termed as value of time. However, the great concern is that

economic condition of travelers is significantly heterogeneous, a use of single value of time

for quantification of congestion cost may be inappropriate. It is therefore necessary that in a

survey, a representative sample of travelers need to be considered based on their economicclass. In this research, some the congestion cost can be estimated for some key scenarios as

well that are based on particular transport improvement interventions.

3. Project Methodology

The project methodological framework encompassed the data collection, data processing,

data analysis and the final step of congestion estimation and valuation. Figure-1 presents this

framework.

The project study area selected is located on one of the busiest routes of Karachi having

different significant characteristics. It is the highway feeding to all the industries located in

the BQATI and LATI area. This highway also leads to Hyderabad city and interior Sindh.

The oil terminal port is also located in this stretch. To add to it, this stretch also experiences

high amount traffic accidents (Road Traffic Incident, 2010). Furthermore, this stretch has

never been functioning at its design speed, which is in any case no less than 70Km/Hr. All

this, adds significantly to the justification of selecting this as the case for traffic congestion

study presented in this report. The selected stretch is in between the two intersections i.e. Star

Gate at Sharah-e-Faisal (starting point) and Pakistan Steel Mill (ending point). A total of nine

points of representative locations were selected that included Star Gate and Steel Mill as well.

The remaining constitutes seven (7) intersections and two (2) mid links.

A total of nine points were selected on the stretch for the traffic data collection. These points

included the intersections as well as mid-links. They were selected so as to incorporate the

diverse attributes exists within the area of study. For instance, the Malir Halt (2) stop was

used by many pedestrians throughout the day which had its direct impact on the motorized


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transport. Considering only the area between Star Gate (1) and Malir-15 Stop (4), the

localities slightly shifts as the Star Gate is widespread urban locality up to the Malir-15 Stop.

The Malir-15 Stop has lost a very large area of the road due to encroachments, thus

delimiting its stretch to two (2) usable lanes. The Quaidabad mid-link was selected as it is no

more a typical urban area, as represented by the points before this. The sixth stop is the YB

Chowrangi (6), commonly referred as Manzil Pump Stop. It links the Landhi Industrial Area

with the National Highway and in continuation to the Port Qasim region. Likewise,

advancing towards Port Qasim, The FAST University point (7) was the optimal mid-link

point between the YB (6) and Bin Qasim intersection (8). The next two points are Bin Qasim

(8) and Pakistan Steel (9) Intersections located on National Highway. It was also kept in view

that the distance between these points must be within range, so as to divide the 20km stretch

in not so large neither so small intervals. The last two points are purely industrial

intersections.

igure 18

Project Methodological frame+or*

Table 18 ata Collection Points

,o. !eogra5hical %ocation Classification 9ustification

1 *tar 4ate Intersection 4eometrical Intersection5 *tart Point

' +alir 2alt Intersection The geometrical intersection

) +alir 6ala!oard Intersection The geometrical intersection

#nd of 7"aida!ad Bridge +id 8in9 Point 8in9ing r!an and Ind"strial rea

+an:il P"mp Intersection The geometrical intersection

3*T niversit% *top +id 8in9 Point *pan for smooth traffic flo;

< Bin 7asim Port Intersection The geometrical intersection

= Bin 7asim +id 8in9 Point *pan for smooth traffic flo;

> *teel +ill Intersection The geometrical intersection5 #nd

Point

Data Collection

Data Processing

Data nal%sis

Congestion Estimation and its

Valuation


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3.1 ata Collection and Processing

For the analysis of the study area it was very much necessary to have sufficient amount of

data set to perform an effective study which can produce such results on which sound

decisions can be made. The quality of study depends on the amount and types of data, larger

the amount data and different type of data make your study effective and closer to the real

scenario because the clear picture of the study area is revealed only when you have a largenumber of data represented the study points.

igure 28 ata Collection 5hases

The traffic volume data collection, towards the process of congestion estimation, was the

most essential task among the data collection process. The instrumented vehicle carried the

traffic surveillance cameras along with Digital Video Recorder (DVR) for storing the videos

of all cameras simultaneously with its respective time of accuracy up to per second. The

timings was divided into two phases, morning and evening phase with its time interval

between 8:00am to 1:00pm and 3:00pm to 8:00pm respectively. The morning and evening

data hours were considered so as to record the peak traffic hours. The data at each point was

collected for two days, to counter effect the variations.

igure 38 Traffic :olume ata Collection Methodology and Instrumented :ehicle

The delay study articulates about the delay faced by the vehicles while commuting. This

study conducted on the study area with the help of test vehicle running on the route (from

Data Collection Survey

Traffic Volume

Survey

DelayEstimation

Survey

Spot SpeedSurvey

Value of TimeSurvey

Traffic ?ol"me data

Camera 2oldingssem!l%

Traffic *"rveillianceCameras

Digital ?ideoRecorder

4enerator forPo;er *"ppl%

Instr"mented ?ehicle


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Star Gate to Pakistan Steel) to identify the delay causing points as their reasons. Delay is

considered whenever the speed of the vehicle goes below the reference speed. The reference

speed can be taken as the design speed or any achievable speed on the track. The delay time

was calculated according to various reference speeds which were 20, 30, 40, 50 and

60Km/Hr. respectively. Delay is measure in minutes or seconds, the deduction in speed is

caused by many reasons which were detected during survey. Special emphasizes were given

on the accuracy of the delay data thus it was calculated manually as well as through Vehicle

Tracker device installed in an instrumented vehicle. The Figure below shows the delay data

collection methodology.

igure 48 elay ur&ey

For accurateness the survey of spot speed was also performed besides, this exercise was

accomplished with Laser Speed Gun (LSG) which gives the speed of each kilometer travel.

The spot speed survey will illustrate about the variation in the speed as the vehicle travels

from the Star Gate towards Pakistan Steel and vice versa. It also assists in identifying the

speed variation with regard to time. To add to it, the black spots with respect to speed can

also be identified.

Value of Time (VOT) estimation methodology is based on a questionnaire survey which is

used to model commuters mode choice for their daily trips. Multinomial logit model is

estimated for this purpose in which modal cost and modal travel time parameters are used.

The ratio of the co-efficient of these parameters provides VOT value for individuals choosingdifferent types of travel mode. The BIOGEME® has been used to estimate these models. To

achieve this objective, the survey was conducted at several spots along the study area. This

comprised of the road-side interview, people visiting Petrol, CNG pumps and also passengers

travelling in public buses. The snapshots below are taken during the conduct of the survey at

various industries and at road-side. Some are shown as Figure-5.

Reference *peed

• '@ 6m52r

• )@ 6m52r

• @ 6m52r

• @ 6m52r

• @ 6m 2r

Dela% *"rve% +ethodolog% nal%sis Res"lts

• +an"al dela%

*"rve%

• Trac9er

Data!ase

•

DifferenceReference

*peeds

• r!an and

Ind"strial area

anal%sis

•Reasonsca"sing

dela%s

• Dela% ca"sing

points


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igure #8 :7T $uestionnaire ur&ey at Road side and industries

4. ome !eneral Traffic Trends

The analysis of traffic volume data revealed various facts through inclinations, drifts and

floats. The analysis of traffic volume data describes the trend of different significant values,

like variation of data along the stretch, location and time. The Figure-6 shows the hourly

volume of all vehicles on the urban and industrial area on study stretch having four mid-link

in each. The traffic hourly flow observed in Figure-6 were approximately from Airport to

Malir Kalaboard and then slightly very from Kalaboard to Malir-15. The highest peak found

in urban area was in evening. Similarly in industrial area shown in Figure-6 there was no

sharp peak observed but three mild peak were perceive in three different timing; morning,

afternoon and evening. This shows that the morning peak is of incoming timing of job second

is lunch and other official activity and the third one is outgoing timing from job afterwards

the volume decreases continuously.

A drift in vehicle modes was seen in the urban and the industrial area as more number of

private vehicles was found in the urban zone while oil tankers and trawlers comprised huge

percentage in the industrial zone. This is also seconded by intuition as tankers hub along with

the pumping station near the industrial stretch upsurges their movement while the absence of

residential area and other local facilities clarifies the reduction in the private-vehicle-

movement. In contrary to it, the presence of educational institutes, commercial centers, user

facilities and high density residential units increase the amount of private vehicles users over

the stretch. In the urban area, amongst these modes the private vehicles constitute the most of

it with bikes having the greatest share of the percentage in total and following it are the cars


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T-RTC Brief Research Report Page =

see Figure-7. The reasons behind this are the easy affordability and relaxed on-installments

availability of bikes for all. Public buses comprises up to one-fifteenth portion of the total

modes. On the contrary, within the industrialized zone premises the percentage of bikes

shrinks to less than 10 percent whereas trucks and trawlers constitute nearly one-half of the

traffic. This can be seen graphically below in Figure-7.

igure '8 :olume 5er hour on different times of day

igure "8 Percentage Mode share in /rban )rea ;left< and Industrial )rea ;right -

'@

'1 -

''

: o l u m e ; 5 e r h o u r <

Time based :olume ;/rban<

IRP0RT T0 +8IR 28T

+8IR 28T T0 68B0RD

68B0RD T0 +8IR 1

+8IR 1 T0 7IDBD

@

1@@@

'@@@

)@@@

@@@

< -= > -1@ 11 -

1'

1) -

1

1 -

1

1< -

1=

1> -

'@

'1 -

''

: o l u m e ; 5 e r h o u r <

Time based &olume ; Industrial <

7IDBD T0 +AI8 P+P

+AI8 P+P T0 3*T $I

3*T $I T0 P0RT 7*I+

P0RT 7*I+ T0 P6I*T$ *T##8

Cars'=

Bi9es


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T-RTC Brief Research Report Page >

is industrial so the influence of industrial area is greater in study area. The length of study

stretch is about 20 Km and the length comes under urban zone is about 7.6 Km and 12.4 Km

in industrial zone.

This study also aims to identify the causes that create the delay in the traffic. The

observations of reason of delays were taken during the survey conducted in various timing of

day. There are number of reasons that were highlighted during the survey. The common

reason of traffic stream delay was the improper implementation of traffic control measure,

like Signal delay, road width bottle neck, illegal parking these reason are frequently observed

in the study stretch. Table-2 shows the reasons of delay along the study stretch and there

contribution on the total delay. The delay reason distinguished on the basis of delay

occurrence in the test run of vehicle in the survey the technique of performing survey

discussed earlier in Section-2.

Table 28 ata Collection Points

igure (8 Percentage of delay

occurred in urban and industrial area

All the data from the questionnaire survey, which was comprised of about 1,000 samples was

fed into the BIOGEME® to estimate multinomial model. The estimated model is based on

two parameters travel time and travel cost. The VOT obtained for class of commuters

travelling in different transport modes is summarized in Table-3. The obtained values of VOT

are compared with research conducted in India in 2009, where for average individual person

R-)7, =-I!>T)!- ?

/rban )rea

#ncroachment at +alir 1 intersection ).)

Rail;a% line 2"mp near +alir 2alt >.>

0thers


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T-RTC Brief Research Report Page 1@

this is estimated as Rs 70/hr (Pakistan Rupee). In Bangladesh, for Dhaka city, the average

VOT has been found equivalent to Rs 70/hr (Pakistan Rupee).

Table 38 :alue of time for different Modes

Vehicle operating cost, in complaint to traffic congestion has always been an underestimatedparameter. The data for vehicle operating cost model involves vehicle modes and their fuel

consumption rate. This requires, as mentioned above, test runs of each vehicle modes to get

the fuel consumption rate per hour. The runs were conducted separately for the various types

of fuels used by vehicle. For this study, consumption of fuel is considered as only factor

contributing towards vehicle operating cost. Data has been collected for this purpose for

different types of mode prevailing on the road stretch. Fuel consumption has been determined

for each type of mode and then conversion has been made to obtain unit values such as

Rs/min. Table-4 presents these values for each type of mode.

Table 48 uel Consum5tion ;Rs. @min< for different Modes

M7.- :7T

CR =<

Ta/i =@

0ffice ?ans


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#. $uantification of Congestion %e&els

There were three different criterion selected to describe the facility which includes Level of

Service (LOS), Road Condition Index (RCI) and Travel Time Index (TTI) these all parameterwere used to somehow quantify level of traffic congestion.

According HCM 2000, the computation of LOS through travel speed measured by the

following Table, which describe the level of service of urban street, classified by the FFS of

the facility. The LOS computed through this technique come out, LOS D for urban area and

LOS C for industrial area see Table-5. After wards the overall level of service provided by

the facility is come out D.

Table #8 %7 estimation through s5eed A1B

Table '8 Com5uted %7 for study area

tudy tretch ;*m@h</rban treet

Class

)&erage Tra&el

5eed ;*m@h<%7

/rban )rea =@ I ) D

Industrial )rea =@ I ' C

Com5lete )rea =@ I @ D

Roadway congestion index is a determinate to measure the difference between the actual

traffic volume and the roadway capacity. It demonstrates the loading condition of the road

due to traffic. For instance, a Roadway Congestion Index (RCI) of less than 1 describes that

operating conditions to be feasible, whereas any value exceeding one explicitly illustrates the

link to be overloaded and encumbered as it exceeds further. The Table below shows the

calculation for our study stretch and the Road Condition Index (RCI) was found to be 2.36.

/rban treet Class I II III I:

Range of reeflo+ s5eed ;< DE to "E

*m@h

"E to ##

6m@h

## to #E

*m@h

## to 4E

*m@h

Ty5ical (E *m@h '# *m@h ## *m@h 4# *m@h

%7 )&erage Tra&el 5eed ;*m@h<

) )>-@ )'-1

C @- ))- '=-)> ')-)'

)'-@ '-)) ''-'= 1=-')

- '-)' '1-' 1


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T-RTC Brief Research Report Page 1'

Table "8 sho+ing the RCI &alues for the stretch

The ratio of travel time in peak hours to the travel time in free flow condition is called travel

time index. First the travel time index of each individual midblock is calculated and then the

average is taken to calculate the overall travel time index of the study area. Average travel

time of each midblock is obtained with the help of average car technique, which was

performed to estimate the total delay faced while travelling. The Table shown below

represents the travel time index of the study spots in the given stretch on national highway

from the Star Gate to Pakistan Steel. Approximately all the spots having similar travel time

index (TTI) 1.75.

Table (F Table sho+ing tra&el time indeG.

Midblock Free Flow Time

minActual Time (min) TTI

*tar 4ate to +alir 2alt 1.=@ ).1 1.>1

+alir 2alt to 6ala!oard @.@

+alir 1 to 7"aida!ad ),@@ 1.@

3*T niversit% To Port ,>= '.=@ @.>


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T-RTC Brief Research Report Page 1)

'. $uantification of Congestion Cost

The quantification of traffic congestion is the eventual outcome of this project. It imitates the

tangible upshot of the damage due to traffic congestion. The total traffic congestion cost

quantification also includes indirect cost of traffic congestion, besides the direct cost of

congestion as mentioned above. This indirect cost comprises of the loss to business and

industries, ecological injuries and environmental degradation. In this study, indirect cost is

not the focus, however, attempt has been made to find direct cost of traffic congestion, which

comprises of vehicle operating cost and opportunity cost.

The estimated VOT values are utilised to measure opportunity cost component of traffic

congestion cost. Fuel efficiency and fuel consumption data for different types of modes,

prevailing in that road stretch, was gathered to estimate vehicle operating cost component of

traffic congestion cost. Equation (1) and (2) represents the expressions utilised to compute

opportunity and vehicle operating costs.

∑ (1&

Where, OC= Opportunity Cost of traffic congestion, VOT= Value of time for specific modem, Delay=travel delay in time units observed for mode m (estimated at some referencespeed), V=number of vehicles of type m per day, Vocc= Average vehicle occupancy forspecific modem.

! ∑ " ('&

Where, VOC= Vehicle operating Cost, #$= Fuel cost in Rs/hr for specific mode m, V andDelay have the same meaning mentioned earlier and L= length of stretch in Km. Where,#$is calculated using equation (3).

" ∑ %"&'( ")'( *'(+,'( ()&

Where, #c-= Fuel consumption quantity in litres/km or Kg/km of specific mode m, #./0=fuel price of specific fuel types Ft = 1, 2 and 3 such as CNG, Gasoline and Diesel,

respectively in Rs./litres or Rs/kg. 1/0 =proportion of specific mode type m using a particularfuel type for travelling on that road stretch.


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The collected data was compiled according to the parameters described in equations (1), (2)

and (3). The average values of the volume collected at 9 different locations were given in

Table-9 in vehicle/hour. The Table-9 also provides observed values of vehicle occupancy for

each different type of mode travelling on the stretch of the segment. The travel mode, bike

(motorcycles) contributed largely in the traffic stream and then all other vehicle types.

Table D. Classified &olume and &ehicle occu5ancy

ModesVolume

(vehicle / hour)Vehicle Occupancy

Car 1,')< 2.2E

TaGi '@@ 3.2E

7ffice :an @< 13.EE

Three =heeler 1=@ 2.EE

i*e 1,' 1.2E

Public Trans5ort 3(.EE

Truc* 4D4 4.EE

Table-10 describes the average speed of classified modes on that road stretch. The road is

classified as major urban arterial road, which basically fulfills the purpose of mobility and the

design speed is around 110 km/hr. The average speed of the road clearly depicting the

prevailing condition of the arterial that is partly due to the deteriorated pavement structure

and partly due to the increased demand, both of these factors in combination creating frequent

traffic jams along the road stretch. Table-2 also shows estimated travel delays for each

representative travel mode at two reference speeds (i.e. 20km/hr and 30km/hr). The Table

demonstrates that the delay of a particular mode and reference speed is directly proportional

but the increment of delay is not similar among modes which describe the mobility

characteristic of that mode.

Table 1E. )&erage s5eed and a&erage delay under reference s5eed.

Modes

AverageSpeed

(Km/ hr)

Avg. Delay (Min/Veh)

at ref. speed 20kph

Avg. Delay (Min/ Veh)

at ref. speed 30kph

Avg. Delay (Min/ Veh)

at ref. speed 40kph

Car @ = 1@ 11

TaGi = 11 1)

7ffice :an ' >. 1'. 1)Three =heeler '= . )@ )

i*e ) 1@ )@

Public Trans5ort '> 1).> 1>.) )=

Truc* 2' 1'.( 3E @


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The value of time was also estimated in this study using an indirect approach, more details of

this can be seen in Section-3 of this report, however, VOT values are shown with respect to

mode users in Table-11. Average fuel consumption quantity of classified modes and their

proportions (i.e. µ1, µ2, µ3) are also shown in same Table. Fuel consumption quantities are

estimated by running various test vehicles of same mode type on the road stretch. The

proportions of vehicles using different fuel type is determined based on physical examination

of various travel modes based on sample size of 300 vehicle of each mode type.

Table 118 :ehicle classified :7T +ith Hfactor and fuel consum5tion uantity.

Modes VOT

(Rs/hr)

µ1 µ2 µ3 FcqCNG(kg/km)

FcqGasoline(lt/km)

FcqDiesel(lt/km)

Car =< @. @. - 1 1@-1'

TaGi =@ @.) @.)< - 1=-'@ 1@-1'

7ffice :an


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Table 12. Total Cost of direct traffic congestion 5er day in Pa* Ru5ees

The simple extrapolation of the traffic congestion cost for whole arterial network of Karachi

city can be done by calculating per km cost for the stretch under study, which is around 1.86

Million USD per Km per year. This cost needs to be multiplied with the existing length of

arterial network of Karachi i.e. 1,300 Km. This give total cost of 2.5 Billion USD per year

for whole Karachi that is equivalent to 1.5% of total GDP of Pakistan. Figure-8 presents

extrapolation of the value obtained for Karachi for future 10 years based on 10% constant

inflation rate.

igure (. -Gtra5olation of traffic congestion cost of 6arachi in future years

Figure-8 indicates that in year 2018, (which is 5 years from now), the Karachi congestion

cost will exceed 4 billion USD per year, which is representing 11% of the total GDP share of

Karachi at that time. The detailed report contains estimation based on other reference speeds

which will be available soon at IMC website (www.toyota-indus.com).

2 .

5 0

2 .

7 5

3 .

0 3

3 .

3 3

3 .

6 6

4 .

0 3

4 .

4 3

4 . 8

7

5

. 3 6

5 .

8 9

6 .

4 8

7 .

1 3 7

. 8 5

@

'

=

1@

2E12 2E14 2E1' 2E1( 2E2E 2E22 2E24 C o s t i n 4 i l l i o n / . 5

e r I e a r

ears

Traffic Congestion Cost -Gtra5olation

>eads %oss in Million P6R@day

755ortunity Cost ;7C< '."11

:ehicle 75erating Cost ;:7C< 3.123

=ear and Tear Cost ;1E? of :7C< E.34"

Total Cost 1E.1(


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T-RTC Brief Research Report Page 1<

". Conclusion8

The research study estimated the traffic congestion cost of the major urban arterial of

Karachi. In addition to this, utilized the estimated cost to extrapolate the congestion cost of

all major arterials of Karachi and using GDP, to estimate the cost of other major cities of

Pakistan. This research is first of its kind in the country, and paves way for its further

enhancement in other parts of country to find out the scale of the problem caused by traffic

congestion. The estimated cost is significantly high especially when compared to the GDP.

Therefore, as happened in the Western World, serious mitigation steps needs to be implied

here as well. Furthermore, this is the loss incurred due to traffic congestion not included the

cost associated with indirect costs. It indirect cost is also estimated, that will present the more

drastic image of this issue.

(.

)c*no+ledgements8

The first time of its kind in Pakistan, the research (TRTC) was funded by Indus Motor

Company Limited and the Department of Urban & Infrastructure Engineering, NED

University of Engineering & Technology, Karachi uses its expertise to achieve the objectives

set out for this study. We are particularly grateful to the Sindh Police Department for

providing necessary security arrangements at the data collection stage. We also thank to Civil

& Urban Engineering undergraduate students who took part in data extraction and tabulation

process.

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