Conference PaperPDF Available

Measuring the Economic costs of traffic congestion

Authors:
Measuring the Economic Costs of Traffic
Congestion
Jayasooriya, Chinthaka Sampath
Department of Transport & Logistics
University of Moratuwa
Katubedda, Sri Lanka
121431A@uom.lk
Bandara, Yapa Mahinda
Department of Transport & Logistics
University of Moratuwa
Katubedda, Sri Lanka
mahindab@uom.lk
Abstract—The main focus of this research is to examine
traffic congestion costs related with the road passenger
transportation. With the rapid urbanization, it is evident that
commercial and socio economic activities tending to centralize
only in major cities in a country. This has led to increase in the
number of the commuters daily traveling to the cities from
peripheries. Further the economies grow and the real income of
households & vehicle population are also increasing. As a result,
traffic congestion has become a major issue within urban cities.
Road traffic congestion interrupts and reduces productivity
levels and, it is a symbol of economic inefficiency. In this
research, we present a methodological process to estimate
congestion cost. The process includes data collection to the
analysis of main two cost factors of road traffic congestion. Those
cost factors are workforce productivity time loss & the excess fuel
energy consumption/operating cost. The significance of this study
is that it provides a measure of the real monetary cost of
congestion..
Keywords— Road Transportation; Traffic Congestion;
Congestion cost; Transport Productivity; Economic Efficiency
I.
I
NTRODUCTION
Economic cost of traffic congestion is one of the most
debatable issues in an economy. It would be safe to assume that
economic cost of traffic congestion has been discussed for a
long time and the problem still exists although massive
investments on road transport sector is underway. Traffic
congestion in roads has a massive cost impact on the
production and the general and work lives of many people.
Traffic congestion has not only impacted passenger
transportation but also the freight transportation. Most
developing countries including Sri Lanka suffers an enormous
financial and labour time losses due to road traffic congestion..
This loss was estimated at Rs. 32 billion rupees per annum.
This has risen up to Rs. 40 billons per annum in 2012[1]. That
is approximately 1.5% from the Gross Domestic Production
(GDP) of Sri Lanka.
Fundamentally road traffic congestion occurs when a
volume of traffic generates demand for road space greater than
the available road capacity (supply). Although the capacity of
the city road network increases by 2-3% annually on average, it
is incapable of handling increasing road traffic flows at the rate
that is demanded, which is around 10% increase annually [1].
According to automobile registration records in Sri Lanka,
Fig. 1. Variation of vehicle population in Sri Lanka
the automobile ownership is more than 6.3 million. In peak
hours, more than 3 million automobiles use road network all
over the country. Notably, more than 0.25 million of
automobiles are inbound to Colombo city [2].
According to Fig. 1, the number of vehicle imports/vehicles
used with per capita is significantly rising in 2011 & 2015. The
growth of vehicles per 1000 people from 2008 to 2015 has
risen from 171 to 305 [2].
The economy of Sri Lanka has been suffering from rising
economic cost from road traffic congestion. These costs
include waste (loss) of time, fuel economy and wear and tear of
automobiles and accidents. Most significant factors
contributing to the problem were poor city planning,
inappropriate public transport facilities and insufficient traffic
system. However, increase in demand for private automobiles
and high usage can be identified as a root cause of the problem.
Private motorized transport share has increased due to lower
level of service in public transportation industry (inappropriate
public transport facilities). The use of public transportation has
decreased by 25% from (65%) in 2008 to (40%) in 2016 [3].
The share reduction from public transportation services has
been transferred to private automobile like car, van, trishaw
and motorcycle which are mainly contributing to increase
urban traffic congestion. Currently, 0.1 million of automobiles,
0.13 million of trishaws, 0.04 million of vans and 0.37 million
of motorcycles are annually adding to national traffic
aggravating traffic congestion issue [3]. The ultimate result is
that the government has to allocate a considerable portion of
budget on transport infrastructure development projects (to
increase supply and the capacities of transport infrastructures).
However, in most countries including in developed transport
978-1-5090-6491-5/17/$31.00 ©2017 IEEE
systems, public investment in road capacity improvement is not
adequate to curtail the growth of traffic volume [1]. The result
has been a relentless increase in traffic congestion. Thus, it is
important to estimate the economic value of traffic congestion
in Sri Lankan context which will help policy decision making
in resource allocation in road infrastructure investment.
According to experts, in peak hour, traffic flow speed from
Colombo city limits to sub urban cities has reduced 28kmph to
9kmph in 1997-2001 period [5]. In addition peak hour traffic
flow speed within city limits has reduced 32kmph to 6kmph. It
is a common situation for most road networks in urban centres.
Furthermore, though Expressways provide travel up to
100kmph speed, when inbound/ outbound from urban regions
vehicles spend more than 2 hours to travel 20 km. Acceleration
& deceleration of vehicles lead to increase in operating costs in
terms of fuel, tires and brakes. Similarly the sound effects of
road traffic congestion, safety, discomfort and unreliability of
the journey and more importantly the time costs to commuters,
low level utilization of vehicles and interruptions to the freight
flow are major costs of traffic congestion..
Traffic congestion makes both public commuters and
private motorists spend additional time on the roads, paying
extra for fuel. As the number of automobiles on the roads
increases it takes ones journey lengthier and need to spend
more time and cost to reach the destination. Sri Lanka has now
become a county using 21% percent of fuel for transportation
while other neighbouring countries use less (India 7%,
Thailand 16% and Malaysia 19%). Most important fact is that
this percentage has doubled within past three years [6].
Therefor it is good indicator to measure the level of efficiency
of a national transportation system.
A. The scope of the research
The scope of this research is to examine ways of estimating
the economic costs of road traffic congestion in Sri Lankan
context. The purpose of the research is to provide economic
criterion to empirically measure the congestion cost.
This study focused on measuring congestion cost on public
passenger transportation in Galle corridor, a major highway of
Sri Lanka. The study covers the value of congestion delay for
personal travel including occupational and commercial trips.
B. Research Objectives
The Main Objective of the paper is to propose an analytical
method to measure economic cost of traffic congestion
related with passenger transportation. The study achieves
the main objective with the help of few secondary
objectives. They are;
o Propose an analytical method to calculate Value of
Time (VOT)
o Propose an analytical method to calculate Additional
Fuel Consumption (Operation) Cost (Vocc)
Examine current economic efficiency level of road
passenger transport facilities of Fort to Moratuwa section
of the Galle corridor
II. L
ITERATURE
R
EVIEW
Research on Economic costs related to traffic congestion
was carried out by number of researchers using difference
techniques/ methodologies. This paper presents a widespread
review of the literature on measuring the economic costs of
road traffic congestion. Especially those literatures can be
categorized according to data collection methods,
methodologies that used to approach the context, evaluation &
data analysis method, indicators considered, and errors
mitigation methods.
The following formula was adopted to calculate traffic
congestion cost [2].
(1)
Where, OC = Opportunity Cost of traffic congestion, VOT
m
=
Value of time for specific mode m, Delay
m
= Travel delay in
unit time observed for mode m (estimated at some reference
speed), V
m
= number of vehicles of type m per day, Vocc
m
=
Average vehicle occupancy for specific mode m.
(2)
Where, VOC = Vehicle Operation Cost, FC
m
= Fuel cost
Rs/hr for specific mode m, and L = length of stretch in Km.
(3)
Where, Fcq
m
= Fuel consumption quantity in litres/km (or
Kg/km) of mode m, Fp
Ft
= Fuel price of specific fuel types Ft
= 1, 2, & 3 such as CNG, Gasoline and Diesel respectively in
Rs/litres or Rs/kg. µ
Ft
= proportion of specific mode type m
using a particular fuel type for travelling on that road section.
Here, the calculations are carried out separately for difference
mode of transport in the traffic stream and the Opportunity
Cost of traffic congestion and the excess Vehicle Operation
Cost are can separately evaluate. This is ensuring high
accuracy of the output.
Data analyse used by Harriet & Emmanuel, (2013) includes
the following benchmarks;
Traffic Flow Volume Analysis
Key Roads in Order of Congestion Level
Traffic mix distribution
Field data (Primary data) analyse
The researchers have used descriptive statistics of simple
averages to analyse primary data. The study on influence of
the transportation system in Kumasi on Driver’s Productivity
analysed according to trip classifications [3]. The anticipated
and the actual number of trips and income have been analysed
in this research.
The following three curves have been used in evaluating
economic cost of traffic congestion [4].
Supply–demand shifts in response to transportation
investment
Hypothetical bid-rent curve
Conceptual marginal and average travel costs during
congested conditions
It is shows that road traffic congestion reasons first-order
delays and inefficient commuting times. But, these
approximations of cumulative economic impact rely on
valuing the opportunity cost of travel delay.
There are six steps have been used in congestion cost
estimation process [10]. First three steps represent
measurement of traditional user impacts (Trip data, Travel
time and distance data & User travel cost calculation). These
are additional time and expenditure suffered by users as a
result of traffic congestion, and mitigation of those user costs
is reflected to be the primary transportation system
productivity benet of transportation volume upgrading
projects. Other three steps represent measurement of nonuser
economic impacts (Total unadjusted business cost calculation,
Activity data, Statistical estimation). These are changes in
business costs or revenue subsequent from changes in wage
compensation, scheduling, logistics, and market-scale
economies as a consequence of changes in traffic congestion
density.
III. M
ETHODOLOGY
A road section of Galle corridor (A2), (Moratuwa to Pettah)
is selected as the data collection point for this research. It is the
main gateway to Colombo from the Southern parts of the
country and is a highly demanded section for daily commuting
that also integrates various modes and services of
transportation to cater the daily definite requirements of
commuters. Currently there is a huge traffic flow in Galle road
during the peak hours and it is directly causing a higher
economic cost of congestion.
A. Data Collection
Primary data was obtained from a socio economic survey
that was conducted in the Galle corridor. The socio economic
survey includes data related with commuters’ travel distance,
travel time, working hours, willingness to pay values for saving
travel time (if the speed doubles) and from private transport
mode users, fuel consumption quantity of the vehicle & the
vehicle leasing payment related data were obtained. Those data
were used to analyse the value of time of daily commuters,
travel distances, average peak & off-peak travel speeds and
average vehicle fuel consumption quantities. The survey was
conduct for both public and private transport mode users. The
survey on bus passengers was conducted during morning peak
hours and included randomly selected 30 of daily bus
commuters as a sample. The survey on private vehicle users
consisted of a randomly selected sample of 30 for each mode
of private transport.
Secondary data was obtained from Vehicle counts and bus data
received from the Bus Rapid Transit (BRT) surveys conducted
by the Department of Transport & Logistics Department,
University of Moratuwa on the 4th Sep 2014.
B. Data analysis method
Collected data was analysed using MiniTab and MS Excel for
generating descriptive statistics. A formula derived from
literature was used to calculate economic value of time loss
due to traffic congestion with high accuracy. The method is
also reliable as it accounts for different mode of transport
separately. As different modes of transportation separately
considered, the analysis for bus, trishaw & motor cycle
passengers were carried out using willingness to pay (WTP)
values, method 1, with the objective of deducing travel time
(save the travel time). Other data on passengers those who use
cars/vans were analysed using a strategic method (SM),
method 2, additionally to the willingness to pay values. In the
strategic method, the interviewee were asked about whether
vehicle is leased one or not, car type, the leasing institution,
the monthly leasing payment amount which they obliged to
pay and the number of members in the family. These data is
useful to derive the average income of a person in the sample.
IV. R
ESEARCH
F
INDINGS
A. Transport system in Galle corridor
The Galle road corridor comprises of only four lanes to the
both directions which caters bidirectional vehicle traffic
volume of 67,600 vehicles. Among them 3790 are busses
which cater 80% of the road passenger volume. In terms of
physical geometry, the width of some sections of the road is
narrowed up to 20m in the Galle corridor which has increased
the peak hour vehicle traffic up to 2900 PCU and reduced the
average speed up to 13kmph. The peak hour average bus load
factor is 101% which caters approximately 41% of the total
bidirectional passenger demand. Current traffic plan in the
Galle corridor has been effective to a certain extent by
introducing one-way traffic in the parts of the road/sections.
Nevertheless, arguably the cost of the additional distance
travelled by the vehicles outweighs the benefit earned by the
time saving. . It is also found that in most instances
inefficiencies of the utilization of current road space have
adversely effected to increase congestion and lead to passenger
discomfort. It is found that approximately 22% of the time one
lane is blocked and 5% of the time both lanes are blocked [11].
The discipline of the bus drivers is also in a very unappreciable
condition leading to major accidents and congestion. It is
identified approximately 60 bus routes using the Galle corridor.
Feeder bus system also shows grave inefficiencies in
transferring the passengers to the main busses running in the
Galle corridor.
B. Estimation of Economic Costs of Traffic Congestion
a) Vehicle user’s data analysis of Galle corridor
Western Province modal share is shown by Table. 1 & the
number of passenger data are shown in Table. 2. According to
statistical data available in the Department of Census and
Statistics, population growth in Sri Lanka recorded from 2014
to 2016 is 0.19%. If the number of passenger growth has same
value, the average number of passengers for 2016 can be
predicated.
TABLE 2 - GALLE COR RIDOR
PASSENGER DISTRIBUTION
Veh. Type Pax. (2014) Pax. (2016)
Motor cycle 19,949 19,987
3 wheelers 21,823 21,864
Car/Van 33,802 33,866
Bus 119,016 119,242
TABLE 1 - WESTERN
PROVINCE MODAL SHARE
Western Province
Vehicle ty pe Modal
Share
Bus 48.1%
Train 3.4%
Car/van 14.3%
Motor cycle 17.9%
3 wheelers 16.3%
a.
Source: ComTrans JAICA(2014)
b) Analysis of Number of Vehicle in Galle Corridor
The average number of passengers in each mode of road
transportation & the average daily bi-directional traffic volume
variation in Galle corridor data were obtained from the vehicle
counts conducted by Department of Transport & Logistics
Management in 4th Sep 2014. Those data represented in
following Table 3. If we assumed traffic density growth from
2014 to 2016 is 1.5%, the vehicle capacity for 2016 can be
estimated.
TABLE 3 - AVERAGE DAILY BIDIRECTIONAL TRAFFIC VOLUME
Veh. Type #Veh.
(2014)
Avg. #Veh.
(2016)
Pax per
Veh.
Motor cycle 15,345 15,575 1.28
3 wheelers 11,486 11,658 1.88
Car/Van 16,260 16,504 2.05
Bus 3,790 3,847 31.00
c) Analysis of Average Speed of Galle corridor
Sri Lankan government has regulated driving speed by Motor
Traffic (Speed Limits) Regulations, Gazette No. 1763/26 of
2012. It has divided road network into two sections as Built-up
areas and Non-Built-up areas. All automobiles operating on
the segments of the “Built-up areas” are subjected to 50kmph
speed restriction. However, the speed limit with respect to
land vehicles, motor tricycles, motor tricycle vans and special
purpose vehicles operating on the segment of the built-up
roads are specified to 40kmph. All other road segments in Sri
Lanka which are not specified in the schedule to these
guidelines are known as “Non-Built-up areas”. The speed limit
relevant to the road segments of the Non-Built-up areas is
70kmph for all categories of automobiles and 60kmph for all
motor coaches and lorries.
In this research considers only those regulated speed limits as
the maximum utilized speed (design speed) of road sections.
The observed average peak and off peak time are used to
calculate average peak and off peak speeds. Comparison of
these design speeds and actual speeds are represented by the
following Fig. 2 & 3.
Fig. 2. Average Speed Variation of Buses in comparison to the Regulated
Speed Limits
Fig. 3. Average Speed Variation of Private Vehicles in comparison to the
Regulated Speed Limits
d) Vehicle Fuel Consumption variation with speed
a.
Source: http://www.myengineeringworld.net/ ("My Engineering World", n.d.)
Fig. 4. Vehicle Fuel Consumption variation with speed
Fig. 4 describes the relationship between fuel consumption of
the engine and the average speed of an automobile. The graph
is divided into four zones, parallel to four speed ranges,
When considering these figures in relation with Galle road
condition, as shown in Figure. 2 & 3 the design speed varies
between 40kmph to 70kmph and the actual speed varies
between 0kmph to 40kmph. Therefor reduction in speed
influences to increase fuel consumption approximately from
16.66 km/l to 12.5 km/l. This means reducing 24.96% of fuel
economy of the vehicle for a driving distance (km) for a fuel
litre.
Table 4 presents the estimated values for fuel consumption
quantity of each different modes of transport. ‘µ’ factors
denote proportion of each modes of transport according to the
fuel type. ‘µ’ factors are calculated using statistical data
available in the Department of Motor Traffic, Sri Lanka.
TABLE 4 - FUEL CONSUMPTION QUANTITY & PROPORTION OF EACH
MODES OF TRANSPORT ACCORDING TO FUEL TYPE
Modes µ (Petrol) µ (Diesel) µ (Hybrid)
Fcq
(Petrol)
(km/l)
Fcq
(Diesel)
(km/l)
Fcq
(Hybrid)
(km/l)
Car/Van 0.45 0.24 0.31 14 15 16
Three
Wheeler 0.94 0.06 - 28 32
Motor
Cycle 1 - - 47 -
e) Value of Time Analysis
Value of Time of passengers in buses, motor cycles and three
wheelers were estimated using additional willingness to pay
values, if they are provided infrastructure facilities/transport
services to travel with a speed twice as the to the existing
driving speed. Hypothetical relationship with this scenario is
that, when commuters can travel with a higher speed, they can
save additional money which is now spent as fuel/operating
cost & value of productive time loss. In this research we
assume that the willingness to pay values is equal to the values
of these cost factors.
The Value of Time of car/van users are estimated using a
strategic method in addition to the willingness to pay values.
In the strategic method, interviewee were asked financing
method of the vehicle, that is whether the vehicle is leased one
or not, leasing/financial institution, car type, the monthly
leasing payment which they obliged to pay and the number of
family members. A financing organization deciding a car
leasing premium for a person considers number factors such as
Monthly income (Salary script of last 3 to 6 months), Average
monthly expenditure of the family, other income of the family
(Number of people who are doing jobs in the family), loans &
leasing already taken and the age. Financial institutions
basically provide a person 60% of the monthly income of the
family.. Assuming 50% of family members contributes to the
family income and other loans (housing 9.05%)(Central Bank
Report 2015), the following Equation (4) can be derived.
By dividing the average monthly income of a person from the
working (earning) time x number of working days of a month,
the average value of time of a person can be estimated.
Primary data analysis carried out and the result are shown in
Table. 5.
TABLE 5 - ANALYSIS RESULTS FOR THE VALUE OF TIME
Mode of transport Avg. Value of Time
(Rs. / Hr.)
Car/Van (WTP) 78.59
Car/Van (SM) 291.51
Bus 28.56
Motor cycle 38.10
Three wheeler 101.17
f) Calculations of the Opportunity Cost of traffic
congestion
Using the estimated average peak & off-peak speed, the
average time delay for each mode of transport can be derived
(Equation 5).
Time Delay = Travel delay in time units
=
(5)
Using Equation (1) the opportunity costs are calculated.
Results are as shown in in Table 6.
TABLE 6 - ANALYSIS RESULTS FOR THE OPPORTUNITY COSTS
Mode of transport Opportunity Cost (Rs. Mn.)
Bus 3.12
Motor cycle 0.21
Three wheeler 0.22
Car/Van (WTP)
2.00
Car/Van (SM)
7.40
Using Equation (3), the Fuel cost (Rs. /km) (for the bus
operation it is used average operating cost) is estimated. Using
fuel cost values and the Equation (2), the excess fuel
(operating) cost was derived. The analysis results are shown in
Table 7.
TABLE 7 - ANALYSIS RESULTS FOR EXCESS FUEL/
OPERATING COST
Mode of
Transport
Excess fuel Cost/ operating
cost (Rs. Mn.)
Bus
4.73
Motor cycles
0.10
Three wheelers
0.03
Car/Van
0.65
C. Comparison of economic cost of traffic congestion
Vehicular traffic congestion has directly impacted on
workforce productivity levels and the fuel consumption. In
this research economic values for traffic congestion to assess
the potential impacts were estimated. Table 8 shows a
comparison of economic cost of traffic congestion in Galle
corridor for each mode of transport separately. As for
opportunity cost of congestion, it represents the value of loss
of productive time and the excess fuel cost. Excess fuel cost
represents the cost for additional fuel due to traffic congestion
in Galle corridor (for the bus operation it is the additional
operating cost). This research employed two methods to
analyse car/van passengers’ related data. However, the final
results of two methods show a higher variation. In the first
method, commuters’ willingness to pay values represents how
they believe worth of their wasted time due to traffic
congestion. WTP method derived a lower value for VOT (Rs.
2.65 Mn.). In the second method (SM), analyses their actual
worth of wasted time and the method help deriving the actual
value of economic cost from the traffic congestion (Rs. 8.05
Mn.).
TABLE 8 - COMPARISON OF ECONOMIC COST OF TRAFFIC CONGESTION
IN GALLE CORRIDOR
Mode of
Transport (Rs.
Mn.)
Bus
Car/Van Motor
Cycle
Three
Wheel
WTP
method SM
Opportunity
Cost 3.12 2.00 7.40 0.21 0.22
Excess Fuel
Cost 4.73 0.65 0.65 0.10 0.03
Total Cost for
Day 7.85 2.65 8.05 0.32 0.25
Total Cost for
Year 2865.25 967.25 2938.25 116.80 90.16
Average Monthly = (Monthly x 100 x 1 . (4)
Income of the leasing 50.95 (# Family Members x 0.5)
Person payment)
V. C
ONCLUSIONS
Sri Lanka is a country suffering from severe traffic
congestion mainly in urban centres. Traffic congestion has led
to greater losses in the national productivity. Increasing
population in the urban areas and, as a result, increasing their
mobility needs via public and private transport modes coupled
with insufficient infrastructure, poor traffic control, complex
land use pattern, hazardous driving behaviour and high density
of road users create heavy congestion in urban roads, costing
more to the society in terms of longer commuting times
(reducing workforce productivity), excess operating (fuel)
costs. The personalized transport modes and the informal
sector such as cars/van, three wheelers and motorcycles are
significantly contributing to this congestion. Metropolitan
transport system is a critical feature that may expedite and limit
urban economic growth depending on the degree of its
management for effectiveness and efficiency.
A. Summary of the Findings
The growth of vehicles per 1000 people has risen from 171 to
305 between 2008 and 2016 in Sri Lanka. The alarmingly the
bus to private vehicle ratio is 1:11.37 meaning that 91.92% of
private vehicles in operation and shared the road space with
public transport in the selected urban corridor. Nevertheless,
in term of bus passenger capacity to private vehicle ratio is
1.6:1 that means additional 61.16% of passengers are catered
by public bus transportation. This indicates that personalized
transports significantly contribute to the traffic congestion
making significant losses to the economy. Fig. 5 shows the
comparison of daily opportunity cost, excess fuel (operating
cost) & total cost of traffic congestion.
Fig.5 Daily Traffic Cost Variation
Figure 6 & 7 present mode-wise annual total economic cost of
traffic congestion variation for the selected road corridor.
Higher percentage of annual traffic congestion cost is born by
bus transportation & car/van transportation.
B. Limitations of the Study
Research uses secondary data (vehicle counts) taken from the
year 2014. However, the primary data collected by travel time
surveys are from the year 2016. To tally passenger capacity
for year 2016, the population growth rates of Sri Lanka
between 2014 and 2016 was adopted. It is also assumed that
the traffic density growth of the selected corridor for the
analysis is 1.5% with respect to the year 2014. Those
assumptions might reduce the accuracy of the final numerical
results; however the methodology of congestion costs
estimation remains unaffected. In addition, in calculating
VOT, the upper boundaries of speed limits were considered as
the design speeds of each road section.
Furthermore, it was assumed that a person can allocate 60%
from the monthly total income of the family for the purpose of
loans & leasing in the calculation of income of commuters
A
CKNOWLEDGMENT
I also take this opportunity to express my deep regards and
gratitude to the Head of the Department of Transport &
Logistics, and all the lecturers & staff members for giving all
kinds of supports, advices and comments on the research work
and allowing us using department data in this research.
R
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Fig. 6 - Annual Total Traffic
Congestion Cost Variation in Galle
Corridor (Using WTP method)
Fig. 7 - Annual Total Traffic
Congestion Cost Variation in
Galle Corridor (Using SM)
... Total vehicle population in Sri Lanka has dramatically increased with a compound annual growth rate of 10 % between 2012-2016 (Jayasooriya and Bandara, 2017). According to vehicle registration records in Sri Lanka, in peak hours, more than 3 million automobiles use road infrastructure all over the country. ...
... Low travel speed due to the traffic congestion results in high emissions to the environment, loss of productivity and production, deteriorating the health capital and increasing the other costs components such as adaptation (for example living closer place to the city). All these have resulted in a massive environmental, financial, health and man-hour loss, waste of fuel, wear and tear of vehicles (Jayasooriya and Bandara, 2017). ...
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... In the international context, this risk transition has been demonstrated in the dimensions of economy and health (Jayasooriya & Bandara, 2017;Weisbrod et al., 2003). The negative economic impact of road traffic congestion, considered a type of systemic risk, has been the focus of international research for many years. ...
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A discrete network design problem (DNDP) is conventionally formulated as an analytical bi-level programming problem to acquire an optimal network design strategy for an existing traffic network. In recent years, multimodal network design problems have benefited from simulation-based models. The nonconvexity and implicity of bi-level DNDPs make it challenging to obtain an optimal solution, especially for simulation-related models. Bayesian optimization (BO) has been proven to be an effective method for optimizing the costly black-box functions of simulation-based continuous network design problems. However, there are only discrete inputs in DNDPs, which cannot be processed using standard BO algorithms. To address this issue, we develop a hybrid method (BO-B&B) that combines Bayesian optimization and a branch-and-bound algorithm to deal with discrete variables. The proposed algorithm exploits the advantages of the cutting-edge machine-learning parameter-tuning technique and the exact mathematical optimization method, thereby balancing efficiency and accuracy. Our experimental results show that the proposed method outperforms benchmarking discrete optimization heuristics for simulation-based DNDPs in terms of total computational time. Thus, BO-B&B can potentially aid decision makers in mapping practical network design schemes for large-scale networks.
... It's a serious and genuine problem whose solution is yet to be explored properly. Traffic congestion cause increased fuel loss (Wurhofer et al., 2015), more vehicle operating cost (Jayasooriya and Bandara, 2017), air pollution (Lu et al., 2017), noise pollution (Alam, 2011), psychological stress of the commuters and drivers (Lucas and Heady, 2002) and is characterized by loss of time of the commuters (Verhoef and Rouwendal, 2004). ...
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Daily commute is a part and parcel of the human life but traffic congestion is a perpetual urban problemacross the world. This paper measures the extent of traffic congestion in Guwahati, the largest city ofAssam, India. Shifting commuters from the private vehicles to the public transport is often cited as thepolicy prerogative of transportation planning authorities across the world. The cited benefits are less trafficcongestion, lesser environmental and energy issues. To achieve this end measurement of the extent oftraffic congestion as well as understanding the modal choice of the commuters need are prerequisites andshould be at the centre of policy making. Based on a sample of 400 daily commuters across the Guwahaticity and with the help of a structured bi-lingual questionnaire we collected the responses on five pointLikert scale. We found that traffic congestion is prevalent in all the three zones of Guwahati as per theTravel Time Index criteria but its of “mild traffic congestion” category in all the three zones of “traditionalareas”, “newly established areas” and the “commercial areas” of Guwahati as per the Speed PerformanceIndex.
... This growth has created social, environmental, and health issues, such as traffic congestion, air and noise pollution, greenhouse gas emissions, accidents, and physical inactivity among users. All of these factors are adverse to society in terms of costs, economic productivity, and environmental externalities [8]. A holistic spectrum of areas has been studied in order to mitigate losses derived from these factors and enhance the sustainability of the transport sector, including use of technology, infras-tructure expansion, incentives for driving behavioural changes, limitations of car traffic, and aggressive land use policies [9]. ...
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Electrification of mobility is paving the way in decreasing emissions from the transport sector; nevertheless, to achieve a more sustainable and inclusive transport system, effective and long-term planning of electric vehicles charging infrastructure will be crucial. Developing an infrastructure that supports the substitution of the internal combustion engine and societal needs is no easy feat; different modes of transport and networks require specific analyses to match the requirements of the users and the capabilities of the power grid. In order to outline best practices and guidelines for a cost-effective and holistic charging infrastructure planning process, the authors have evaluated all the aspects and factors along the charging infrastructure planning cycle, analysing different methodological approaches from scientific literature over the last few years. The review starts with target identification (including transport networks, modes of transport, charging technologies implemented, and candidate sites), second, the data acquisition process (detailing data types sources and data processing), and finally, modelling, allocation, and sizing methodologies. The investigation results in a decision support tool to plan high-power charging infrastructure for electric vehicles, taking into account the interests of all the stakeholders involved in the infrastructure investment and the mobility value chain (distributed system operators, final users, and service providers).
... (2) (Baqueri et al., 2016), (Jayasooriya & Bandara, 2017) Transport costs per passenger kilometer is derived by dividing the total transport costs from the annual passenger kilometers. = ...
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The land transport sector has direct cost inputs as well as intangible costs arising from traffic congestion, delays and etc. In addition most governments' tax transport inputs and provide subsidies for some operations. Such interventions also affect pricing and encourages development of one mode of transport over another. Better understanding of transport pricing functionalities can lead to improved policies of taxation and subsidies. This research identifies different transport input costs, types of taxes and subsidies commonly observed in transport pricing. It also identified the tangible and intangible costs and discusses how they can be included in the estimation of overall cost of mobility. The methodology allows the computation of overall cost of mobility of any given community as well as the cost for each mode of transport which will enable policy makers to influence the supply of the respective modes of transport towards reaching a lower overall cost of mobility.
Chapter
This paper seeks a straightforward question: Is the infrastructure improvement or demand management strategy is a better solution to traffic congestion problem to the region’s economy? This research focussed on the economic evaluation of congestion relief measures under heterogeneous traffic condition. A comprehensive methodology is developed and estimated the congestion cost annually for urban links in the Indian city. Road widening and private vehicles that are prominent in India such as two wheeler and car shift to public bus are considered separately as the relief measures. The percentage reductions of congestion cost with the relief measures and normal congestion state are compared. The result shows that the percentage reduction of congestion cost due to road widening is 55.55%, two wheeler shifts to bus is 94.99% and car to bus is 94.92%. Benefit cost analysis is conducted to find the optimal solution for congestion problem. The b/c ratio for road widening is 1.1, shift of car to bus is 2.5 and mode shift of two wheeler to bus is 2.9. The analysis revealed that the demand management strategy is a better solution compared to road widening for the Indian city.
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This paper consists of a study conducted to quantify the traffic congestion problem. Additionally, it describes the complete methodological process from data collection to cost per minute delay. Traffic congestion issues are frequently observed in large cities of the world where the road facilitiesoperate at capacity limits. To estimate the congestion cost,an arterial route of Karachi (A metropolitan city of Pakistan) was selected that has significant importance due to the industrial and port associated activity. The obtained results indicate that Pak Rs. 1 million are lost daily due to traffic congestion, which is extrapolated for whole Karachi. The amount is a composition of opportunity and fuel consumption costs caused due to excessive delays in congestion.
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ABSTRACT This paper provides key findings from NCHRP Study 2-21, which examined how urban traffic congestion imposes economic costs within metropolitan areas. Specifically, the study applied data from Chicago and Philadelphia to examine how various producers of economic goods and services are sensitive to congestion, through its impacts on business costs, productivity and output levels. The data analysis showed,that sensitivity to traffic congestion varies by industry sector, and is attributable to differences in each industry sector’s mix of required inputs and hence its reliance on access to skilled labor, access to specialized inputs and access to a large, transportation-based market area. Statistical analysis models were applied with the local data to demonstrate how congestion effectively shrinks business market areas and reduces the “agglomeration economies” of businesses operating in large urban areas, thus raising production costs. Overall, this research illustrates how it is possible to estimate the economic implications of congestion, an approach that may in the future be applied for benefit-cost analysis of urban congestion reduction strategies or for development,of congestion pricing strategies. The analysis also shows how congestion reduction strategies can induce additional traffic as a result of economic,benefits. OVERVIEW While it is clear that increasing traffic congestion does impose costs upon travelers and affect
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Does traffic congestion negatively impact the economic growth of metropolitan areas? This article reviews the findings of three research directions addressing this question. First, research on first-order impacts indicates that the economic value of congestion-induced travel delay is tenuous since travelers adapt. Second, research on second-order impacts suggests that congestion slows metropolitan growth, inhibits agglomeration economies, and shapes economic geographies. Third, research on public-sector congestion mitigation policies identifies significant fiscal burdens despite limited success at reducing congestion. In sum, research on individual, business, and public-sector responses to congestion demonstrate a shift from congestion mitigation toward adaptation.
Massive Rs 32 bln loss due to traffic congestion: Transport expert
  • B Sirimanna
Better public transport key to reducing city traffic congestion, experts say
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Traffic congestion and congestion pricing
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R. Lindsney and E. Verhoef, "Traffic congestion and congestion pricing," in Handbook of transport systems and traffic control, ed: Emerald Group Publishing Limited, 2001, pp. 77-105.
An assessment of traffic congestion and its effect on productivity in urban Ghana
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