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Traffic congestion has become a critical concern when its detrimental effects are taken into account. Incremental delay, excessive fuel consumption and higher vehicle emission are thoughtful impacts of traffic congestion. These negative impacts cause substantial economic losses to the transport system. Hence, it is essential to enumerate these impacts in their monetary terms to provide a better economic growth and social welfare to the society. Thus, a comprehensive review on the impact of traffic congestion comprises traffic delay, fuel consumption and vehicle emission has carried out. Moreover, the paper reviews the economic quantification of this three impacts of traffic congestion. Systematic understanding and estimation are possible by the support of empirical data. Hence, this paper discusses the data requirements and data collection methods for the estimation of delay cost, fuel cost and emission cost. A new methodology has proposed to estimate delay cost for the link, intersection and corridor as separate facilities. This paper also focuses on the challenges to be confronted while quantifying the congestion impacts in monetary value and further research direction are proposed. © 2018 Institute for Transport Studies in the European Economic Integration. All Rights Reserved.
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European Transport \ Trasporti Europei (2018) Issue 68, Paper n° 5, ISSN 1825-3997
2
movements. Congestion adversely affects the economy and social well-being of the road
users by wastage of time, deterioration of the health, travel time delay, inability to
forecast travel time, increased fuel consumption which causes air pollution and gas
emission, wear and tear on vehicles, noise pollution and reduction in road safety.
When the transportation system collapses to the condition of traffic congestion from the
normal condition, the financial planning of the transport system increases because it has
to provide mitigation measures such as widening of roads and construction of fly overs
for the smooth flow of traffic. Congestion leads to an economic inflation, referred as an
inconvenience and incremental cost resulting from the interference among road users
(Litman, 2013). Traffic congestion costs constitute both internal and external parameters
in which internal or direct cost is induced by vehicle users while the congested
surroundings generate an external or social cost. Direct cost is borne by the non-
productive activity of road users and extraneous consumption of fuel. Likewise, the
social cost is brought about by external elements in transport system such as air and
noise pollution, accidents and risk from accidents and imposes society as
whole.(Thomson & Bull, 2002).
The review of literatures related to the above subjects reveals that many analyses have
been performed to establish the cost of traffic congestion. Many conventional works
deal with different types of congestion estimation methods such as total cost, marginal
cost and excess burden cost (OECD Transport Research Centre, 2007).Researchers also
recognized the agglomeration externalities to estimate the traffic congestion cost. The
agglomeration externalities concentrates on the estimation of economic activity in the
area and thus by providing different policies to mitigate traffic congestion.(Arnott,
2007).Though, various investigations have been carried out to examine the cost of
congestion, only limited number of studies were made to quantify the economic impact
of traffic congestion. However, this review proposes a framework for emphasizing the
three important impacts of traffic congestion and its economic estimation.
A challenge to transport investors and planners is to mitigate traffic congestion and thus
provide a better economic growth and social welfare to the society. For this, congestion
impacts have to be estimated on their monetary terms. Hence, a comprehensive review
concentrates on three important impacts of traffic congestion namely delay impact, fuel
consumption impact and emission impact. Furthermore, a systematic review of data
requirements and data collection methodology is addressed in this paper. A new
methodology has been proposed to estimate delay cost for the link, intersection and
corridor as separate facilities. The heterogeneity in vehicle and location characteristics
made the cost evaluation technique unique for various countries. Moreover, the
challenges to be addressed while quantifying the congestion impacts and further
research direction are presented in this paper.
2. Evaluation Of Congestion Impacts
The general methodology that has been followed in many studies for the congestion cost
estimation is depicted in the Fig.1. In view of this framework, the three main impacts
are considered. Primarily, the discussion of the congestion impact on traffic delay is
considered and delay cost is estimated by means of the value of travel time. A method
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European Transport \ Trasporti Europei (2018) Issue 68, Paper n° 5, ISSN 1825-3997
5
intersections. A major disadvantage of this method is the prolonged duration which
causes undue strain on the observers. In order to overcome that, video graphic method
can be used.
In video graphic method, high resolution video cameras are placed at two ends of the
section by synchronizing the time and record video to obtain the travel time. By
replaying the recorded video and noting down the time taken to the entry and exit time
of the vehicle in the section. This method has an advantage over the registration number
method because it can capture different vehicle type data accurately with less
manpower. In congested state vehicle occupies on the road fully, video graphic method
provides more accurate data than registration number method.
In floating car method, global positioning system (GPS) and distance measuring
instruments (DMI) can be fitted in the vehicle to obtain travel time and it will give
microscopic features of a travel time component. The manual method using stopwatches
in the floating car survey is also a tool for travel time data collection. For congestion
prevailing under homogeneous conditions floating car method gives the best results and
where as in heterogeneous conditions, several runs have to be taken for different vehicle
types to get accurate data.
Time is an economic commodity, and it helps to accomplish products and services as
valuable items. Generally, travellers prefer to choose the route of less travel time, even
at long distance and high running cost of the vehicle. Since travel time has a value, but
the value of travel time depends on many factors, each of varying importance depending
upon the traveller, the purpose of travel, the amount of time available, the reliability of
having the time to use etc. Hence, data collection should include the factors affecting
their travel journey of different rod users.
Wage rate approach and preference approach are the two distinct approaches in the
economic evaluation of travel time. The common and simple approach to ascertain the
passengers travel time cost is to evaluate the average wage rate of the road user and to
treat it as the time value of his travel. As the trip characteristics changes from work time
journey and leisure time journey, the value of travel time for trips also may vary. A
major disadvantage of the wage rate approach is that, it does not account the trip
characteristics of the passengers and is difficult to find the travel time value for
unemployed road users. Hence, preference survey helps to find the distinct behaviour
and value of travel time for different trips.
In preference survey approach, the travellers are asked to value the price they would pay
for reduced travel durations. The actual rating of travellers would depend upon their
wage rate, personal choice, trip characteristics and purpose of the trip. This include
revealed preference (RP) and stated preference approach (SP) and it is the most
scientific way to estimate the value of travel time .Value of travel time is defined as the
ratio of the marginal utility of travel time over the travel cost. It is done by studying the
choice of people and their preference to reduce travel time and travel cost (Beshears et
al 2008). The RP approach evaluate value of time which is best to explain actual
observed choices (Boter et al, 2005),. Since RP approach uses data collected on real life
choices, the choices as made by individual decision makers are bound by the real
European Transport \ Trasporti Europei (2018) Issue 68, Paper n° 5, ISSN 1825-3997
6
constraints confronted by those same decision makers. Researchers have contributed
value of travel time through RP survey method(Román et al., 2014),(Fezzi et al,
2014).Revealed preference survey is not adequate in case where a new transport
invention is under considerations, which necessitates the use of hypothetical scenarios.
In Stated Preference approach, information about decision maker’s preferences is
elicited by using specifically designed hypothetical situation. In SP approach, all
passengers were capable to make choices about preferred travel options and also able to
provide justifications for their choices(Jiang &Morikawa, 2004). Moreover, it is
possible to control the choices offered to respondents and thereby ensure data of
sufficient quality. This method permits generation of multiple observations per
respondents and are also asked to consider a number of situations in a set of option to
maximize their utility in this choice of transport system. SP method is considered as a
better method for value of travel time estimation because the travellers themselves
evaluate different trade off possibilities between travel time and travel cost. Research
has contributed to value of travel time through SP survey(Tseng &Verhoef,
2008),(Calfee& Winston, 1998). The underlying principle behind the preference is that
the results will reflect the revealed and stated behaviour of the preference of the people,
which is therefore nearer to the reality.
3.2 Challenges and mitigation for delay cost data collection
Measuring traffic volume count for roadway sections under varying traffic, traditional
data collection techniques such as manual and mechanical method or advanced data
collection techniques using sensors can be used.
Even though, several methods are available for travel time data collection, suitable
method need to be selected for travel time data collection under congested conditions.
For homogeneous and lane based traffic conditions, travel time data has to be collected
for the same type of vehicle, for that any of the methods such as a registration number
method, video graphic method and floating car method can be used. But in
heterogeneous and non-lane based traffic conditions, vehicle has to perform stop and go
conditions with available space of road stretches, and travel time differs for different
types of vehicle. Hence, travel time estimation by video graphic method gives more
accurate data than floating car method and registration number method in terms of
manpower and several runs for floating car.
In computing the value of travel time from the field, the challenges to be confronted as
that questionnaire has to be designed in such a way that it includes the trip
characteristics, trip maker’s characteristics, and vehicle related characteristics and
transport service characteristics. Hence, a suitable measurement to be taken for the
information regarding the characteristics of the field while experiencing congestion. To
address that, a pilot traffic survey needs to be conducted to identify the period of
congestion and those temporal variations to be included while making questionnaire.
Even though, underlying uncertainties makes estimation of travel time value more
vulnerable to the transport planners, the temporal variation identification holds good
estimates for economic estimation of traffic congestion delay impact.
European Transport \ Trasporti Europei (2018) Issue 68, Paper n° 5, ISSN 1825-3997
7
4. Delay cost estimation for various Facilities
The road transport network system consists of different facilities and their performance
is different from one another. The economic cost may vary from one facility to another
facility and as a whole. The congestion mitigation measures including widening of the
road and provision of an overpass and under pass to be provide at dis aggregate level for
links and nodes in the road. Hence, it must be essential to study the congestion cost for
different facility. A method is proposed to estimate the delay cost for link, intersection
and corridor as separate facilities.
4.1 Delay Cost Estimation at link
Delay estimation at link is assessed based on the link travel time data during peak and
off peak periods. Even though, several methods are used to find out the link travel time,
registration number method gives the better result (Cambridge Systematics et al.,
2008).But in the congested condition due to several vehicles, video graphic method
provides more accurate data. Volume count for link can be done by either manual, video
graphic survey or any advanced data collection techniques. The link delay cost
estimation formula is shown in the equation 2.
iab
n
iablink votvtd ,
1
(2)
Where,  is the delay cost at link,  is the travel time delay between points a and b
in the link, v is the traffic volume on the link, ,is the value of travel time for the
link and i is the vehicle type.
4.1.1 Challenges and mitigation for delay cost at link
A number of connected link form a route. As the link is very short in length, the number
of passengers choosing to travel within it is a part of their trip.. Hence, the sample of the
travelers who uses the link is very difficult to identify. Hence there is an uncertainty in
the estimation of value of travel time for the link travelers. In order to mitigate this
stated preference survey has to be adopted in the locality including the link
characteristics such as link length, specified link travel time and frequency of travel
through link.
4.2 Delay Cost Estimation at Intersection
Delay is the effective measure used for the performance evaluation of the intersection
and is estimated as the extra time consumed by the vehicles in traversing the
intersection. The intersection delay cost formula as shown in the equation 3. Video
graphic survey, manual method and registration number methods are commonly
adopted data collection technique of traffic volume and delay at an intersection.
mimi
mi
n
i
k
m
tioner votvdelayd ,,
,
11
secint 

(3)
European Transport \ Trasporti Europei (2018) Issue 68, Paper n° 5, ISSN 1825-3997
8
Where delay is the delay of the vehicle, v is the traffic volume, vot is the value of travel
time, iis the vehicle type and m is the different approaches for an intersection.
4.2.1 Challenges and mitigation for delay cost at Intersection
The value of travel time of travelers varies on each approach and the surveying is to be
conducted for each approach travelers separately .When vehicles move in a mixed
traffic regime, the travel time value of travelers using different vehicle types is also to
be estimated. Due to different approaches in an intersection, an estimation of value of
travel time is a complex task. Hence, Value of travel time for intersection associated
with stop and go the process of the vehicle can be assessed using the survey at network
level because the field survey is difficult to acquire as the people is an urge to pass the
intersection.
4.3 Delay cost estimation at corridor
Delay estimation at corridor is conformed through traditional travel time measurements.
Floating car method gives the best measure of the delay and volume along the corridor.
Several studies has been conducted to find the delay cost at corridor level(Harford,
2006),(Errampalli et al, 2015), (Well et al, 2011),(Goodwin, 2004),(T. Khan & Mcips,
2013). The equation to find out the delay cost at corridor is shown in equation 4.
i
n
icorridorcorridor votvtd
1
(4)
Where,  is the corridor travel time delay, v is the average volume on the
corridor,votis the value of travel time and i is the vehicle type.
4.4 Discussion
To summarize, the primary impact of traffic congestion, delay and its economic
evaluation are examined in the present study. Systematic methodology proposed for
estimation of delay cost at link, intersection and corridor. Even though, several
techniques are available for data collection, challenges and mitigations in data collection
process and under congested conditions are explained.
5. Fuel Impact
The rapid growth of vehicles in the roadway tends to use the same road section at the
same time subsequently leads to wastage of fuel in the vehicle. The fuel consumption of
vehicles increases in the operation stages of vehicles. Fuel is wasted due to
acceleration, deceleration and idling of the vehicles and it leads to air pollution. This
operation arises in the congested conditions and subsequently leads to wastage of fuel
and indicated as an impact of traffic congestion. It is dignified based on the journey time
and fuel consumption rate during the journey time of the vehicle. Excessive use of fuel
in congested condition increases the road user cost.
Fuel economy of an automobile is the ratio of distance travelled and the amount of fuel
consumed by the vehicles. Stop and go process of vehicle during the congested period
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European Transport \ Trasporti Europei (2018) Issue 68, Paper n° 5, ISSN 1825-3997
10
TABLE 1. Fuel cost study performed in different countries
Author & year Country Formulae and Notations Remarks
Shabbar et al
2014(Shabbar,)
Pakistan  ∗  ∗ 

 FCC fuel consumption cost,
FCfuel consumption quantity Fp fuel price
µ proportion of vehicle FT fuel type, m vehicle type
Representation of mixed traffic
condition.
T Khan et al 2013 Bangladesh ∗∗∗
 C cost of fuel per day,nno of vehicle
A average run per day FEfuel efficiency,ECfuel cost
Applicable in mixed traffic conditions.
Average run per day considered.
Sarkar 2012(Sarkar,
2012)
India    ∗ ∗ ∗ FLCfuel consumption cost
IFL→dling fuel consumption (ml/m),Ddelay (min)
FLfuel rate (Rs/ml),nNo of vehicles
Best suited for idling condition.
Znatak et al 2011(Of
et al., 2011) Egypt   .  . 
AFC average fuel economy in congestion
APSaverage peak period congested system speed
Based on the speed of the vehicle
Darry j et al
2010(“The Economic
Cost of Traffic
Congestio)
Florida   
 AFC annual fuel cost,FC fuel economy
FE fuel economy DVHD daily vehicle hours of delay
APSaverage peak period congested system speed , WD working days
Based on the speed of vehicle and daily
delay of vehicle
Ubillos B
2008(Bilbao-Ubillos,
2008)
Canada    
C financial cost due to additional fuel consumption,
P average price of fuel consumed
D1 length of urban roadway,D2 length of proposed alternative route
G1 average in town fuel consumption of vehicle
G2 average out of town fuel consumption of vehicle
Empirical equation
Theoretical derivation
LUO et al
2007(LUO, JUAN,
SUN, & JIA, 2007)
China    
C fuel consumption by congestion
Ctffuel consumption by transport
Vcaverage speed at congested conditions Vo average speed at normal condition
Compare with normal conditions
European Transport \ Trasporti Europei (2018) Issue 68, Paper n° 5, ISSN 1825-3997
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Fuel consumption from vehicle influences on the road characteristics, vehicular
characteristics and driver characteristics(Ma, Xie et al, 2015),(Boriboonsomsin & Barth,
2010).Therefore, assessment of fuel consumption impact on these factors is
indispensable for better fuel economy and congestion mitigation policies. Hence, fuel
consumption estimation is executed based on these factors by indirect method.
Several empirical models have been developed for the fuel consumption measurements
that linked to operating conditions and road geometric conditions. Initially, research
contributes to fuel consumption model by means of data correlating traffic operation
characteristics (speed) and road geometric characteristics (gradient, rise, fall and
pavement conditions)(A. S. Khan & Clark, 2010).
Pavement quality also affects the fuel consumption rate. The rise and fall of the vehicles
in the roadway increase the fuel consumption. Table 2 depicts the fuel consumption
measurements by various factors and its influence on the fuel consumption rate of the
vehicle.
Afterwards, researchers developed the mechanistic model, which predicts fuel
consumption based on the efficiency of the engine in converting fuel to energy and the
force acting on the vehicles. Mechanistic models are considered as superior to empirical
models because it directly accounts for the individual vehicle characteristics and the
forces acting on the vehicle. But due to prolonged break, this will make an
exception.Mechanistic model mainly considers the vehicle characteristics does not
capture the performance evaluation of transportation system properly.
5.1.1Challenges and mitigation for estimation of data for fuel consumption
Fuel cost is estimated based on the types and number of vehicle experiencing
congestion. Fuel consumed in each vehicle varies depends upon the condition of vehicle
and types of fuel also have a variation such as petrol, diesel and compressed natural gas.
These parameters make an uncertainty in the valuation of fuel cost. Therefore, types of
vehicle and its fuel type has to be collected separately from the field while experiencing
congestion with the aid of manual method and video graphic method.
Fuel consumption depends on the aggressiveness of the driver. Aggressiveness of the
driver may vary from normal conditions to congested conditions; a separate study has to
be conducted to incorporate these conditions.
The fuel cost is highly sensitive to the cost of the fuel. As the fuel cost rise, fuel
consumption cost is also high. Hence, the fuel consumption cost may vary from time to
time depending upon the cost of fuel. Even though, the cost of fuel is fixed one for a
specific time period, types of vehicle and fuel used in the vehicle which embedded in
the congested condition has to be measured to predict the impact of congestion
accurately.
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TABLE 2. Fuel consumption empirical models
Author & year Formulas and Notations Remarks
Sierra
2016(Sierra,
2016)
   ∗ ∗

FC Total fuel consumption of road fleet
VPtotal vehicle population
VKTaverage vehicle kilometer travelled
FE average fuel economy,ivehicle type,Jfuel type
Based on fuel
economy. Vehicle
type and fuel type
are considered
Rakha et al
2011(Rakha,
Ahn)
 

FA fuel consumption or emission rates for every speed
Acc acceleration
S speed of the vehicle
Empirical model
considering the
speed of vehicles
H Wang et al
2008(Wang,
Fu, Zhou, & Li,
2008)
   ∗

FC Trip fuel consumption, I no of bins
i speed-VSP bin index, Ti vehicle trip time speed
FR fuel consumption rate for speed-VSP bin
Mechanistic model
based on vehicle
specific power
Hall
1992(Hall,
1992)
  . . ..
F is fuel consumption at cruising speed (gallons/mile)
V is average speed (miles/hour).
Cursing speed
considered
Chensher and
Harrison
1987(Chesher
& Harrison,
1987)
    
FCis the fuel consumption
S vehicle speed in km/h
IRI international roughness index
RISE rise of the road
FAL L fall of the road in m/km,a0 to a5 are constants
Empirical model
considering vehicle
speed and pavement
characteristics
5.2 Discussion
The fuel impact and its economic evaluation are reviewed. Fuel consumption is
measured by direct and indirect method under prevailing conditions. Direct method
gives the fuel consumption rate of a vehicle directly for normal and congested
conditions. Moreover, while taking the measurements, the representation of the
aggressiveness of the driver and types of vehicles are to be incorporated. In the indirect
method, several empirical relations can be represented as an indicator of congestion
such as average speed of vehicles.
6. Emission Impact
Emission impacts are offseted by the increase of vehicle in the road and also vehciles
tend to use the same road at same time regularly,which leads to severe air
pollution.Vehicular emissions from the traffic are the primary source of air pollution in
the urban area. It consist of pollutants such as carbon monoxide, carbon dioxide, nitrous
oxides and particulate matter (Zhang et al, 2011) and cause health problem to the road
users. Prolonged carbon dioxide emission from the vehicle contributes to global
warming and also significantly affects the ambient air quality.The magnitude and
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intensity of emission depend upon the tarffic activity, traffic performances in the
roadway,fuel consumption rate and emission control srtategy of vehicles.
Traffic Congestion has been indicated as the main contributor to air pollution by
vehicular emission. The negative effect of pollution depends not only on the quantity of
pollution produced, but also on the types of pollutants emitted as well as the conditions
into which they are released. The vehicle emits pollutants such as carbon monoxide,
carbon dioxide, nitrogen oxide and other particulate matters to the environments. Due to
the stop and go conditions during congested periods, vehicle emits more pollutant than
normal steady state conditions.
The emission impact of congestion was calculated based on the fuel consumed and
number of pollutants burned. Emission cost of an automobile is related to the distance
travelled by the vehicle, number of vehicle and emission factors of pollutant that emits
from the vehicle. Table 3 represents the work done in different countries to assess the
emission cost. Emission cost of a particular component derived from traffic is calculated
from the following equations 6.
ff
n
iit celve
1
cos (6)
Where e cost is the emission cost of pollutant, v traffic volume, l is the length of the road
section,is the emission factors per unit weight of pollutant, is the value of the cost
of pollutant and i is the vehicle type.
Data requirements and data collection methodology for emission cost
The emission cost components comprise traffic volume, length of the road section, cost
of pollutant and emission factors. Traffic volume under various traffic flow are
measured any of the methods that explained above. Information about the cost of
pollutant is given by the locality under the survey.
The idea to measure emission factors can be done by two methods namely direct
method and indirect method. In direct method, pollutants emitted from the vehicle are
assessed based on the portable emission instruments which are fitted to the vehicles.On
board emission measurement can be done using instruments. The instrument carrying
vehicle run along the stretch of the road gives emitted particle directly.
PEMS (Portable Emission Measuring System) represents an advanced emission data
collection technology. GPS is enabled in vehicles and this system is made to run along
the study stretches which will bring out the emission data. PEMS provide emission data
from the field and used in many studies,(Wyatt et al, 2014),(Liu et al, 2010),(Tong et al,
2000).
The different factors that influences the vehicular emission are vehicular characteristics,
fuel characteristics, emission control strategy and operating condition of vehicle (Franco
et al., 2013). In indirect method, emission factors are derived using the factors which
affect congestion such as vehicle parameters, driving conditions and fuel consumption
rate.
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TABLE 3. Emission cost study performed in different countries
Author
& year
Country Formulae and Notations Remarks
Alexan
der et al
2013
USA   ,
,Unit cost of emission pollutant
Emission rate of pollutant(Kg per vehicle mile)
P pollutant
Cost is based on
the vehicle mile
travel on the
vehicle and its
pollutant.
Sarkar
2012(S
arkar,
2012)
India    ∗  ∗ 
Pc Pollution cost
Conc concentration of pollutant
(total vehicle population * emission factor)
D delay of vehicle
cost * cost per kg of pollutant
Best for both
homogeneous
and
heterogeneous
traffic
conditions.
Znatak
et al
2011
Egypt  

cost of carbon dioxide
Weight of carbon dioxide
 Unit cost of carbon dioxide
Only carbon
dioxide cost is
considered
The speed of a vehicle is the governing factors that control emissions of a pollutant
from vehicles. Several researches have been done to find instantaneous emission by
considering speed variation (Ahn et al, 2002), (M. Wang et al, 2011).Dynamometer test
is one of the traditional methods to find out the fuel consumption and emission factors
under laboratory conditions. It tests the operational condition of vehicles, namely speed
of the vehicle. It consists of driving cycle that contains stops, starts, and idling of
vehicles which provide the overall weighted average of speed. The limitation of this test
is that it does not account for actual driver and field conditions. The study has been
done to find the emission factors using dynamometer test,(Samuel et al., 2006).
An empirical model for emission was developed by Cappiello et al(2002) considerding
vehicle characteristics.They developed a model for engine out emission and tapline
emision model.The emission level during idling, acceleration and deceleration
conditions of the vehicle in the congested traffic were studied widely. Idling reduction
option for heavy duty vehicles trucks and diesel vehicle were studied by Gaines et
al(2009)(Gaines, Hartman, & Solomon, 2009).
Challenges and mitigation for emission cost data collection
The emission rate of pollutant from the vehicle is an important parameter in the
estimation of emission cost. In direct method, portable emission monitor is fixed in the
vehicles to get the emission rate of pollutant during normal and congested
conditions.Driver behavioral characteristics have a direct impact on the emission of
pollutant. Hence, during the operation of vehicle in the direct data collection technique
various driver behaviors have to be incorporated.
Even though, several methods are used in the indirect method for emission rate
calculation, congestion indicators such as speed and fuel consumption rate are to be
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15
accurately measured in the locality to provide an accurate database for emission
estimation.
Excessive emission occurs when the vehicle undergoes in the congested conditions. The
emission rate of pollutant from vehicle depends upon the vehicular characteristics such
as type of vehicle and emission control strategy of the vehicle .Depending upon the type
and condition of the vehicle the emission varies from vehicle and thus makes remarks in
the cost calculations. Hence, it is essential to provide advance emission control
strategies to every vehicle.
Discussion
The emission impact and its economic evaluation are reviewed. The emission factor of
each pollutant which is emitted from the vehicle is estimated by direct and indirect
method. In direct method, on board emission instrument which can fit on different
vehicle provides the pollutant and its emission rate directly from the field for both
normal and congested conditions. In indirect method, the factors which are influence on
the vehicular emission which is also a parameter of traffic congestion has taken into
account.
Challenges ahead and Research direction
The objective of studying the economic impact of traffic congestion is to propose a
realistic estimate of the economic value of traffic congestion, which is being used for
the evaluation of many highway improvement proposals such as congestion mitigation,
cost benefit analysis and carbon credit for the project. Detailed study and gap to be
addressed are summarized below.
Delay impact causes to travelers to wait in the queue for a period of time and thus by
losing their important time. Therefore, delay is an unwanted travel time for the road
users. Delay cost is estimated by the difference between the average travel time of both
normal and free flow conditions, traffic flow in the roadway and value of travel time for
travelers.
To combine engineering and economic perspective of delay, the value of travel time for
travelers plays a major role. A review has carried out to estimate value of travel time. As
traffic congestion has both spatial and temporal variation, delay cost estimation differs
with respect to these factors. The review stated that preference survey provides most
scientific method of estimating value of travel time at congested conditions. Different
stated conditions are to be added in the surveying process of the value of travel time for
different facilities because of the variation of the travelers’ trip length, travel time for
their trip and frequency of their trip along that route.
Even though, each facility contributes data to the estimation of value of travel time.Data
requirement and data collection of delay cost varies from facility to facility. This review
proposes different data collection techniques, and its advantages and dis advantages and
best data collection process.
More vehicles tend to use the road at the same time, fuel consumption rate of vehicle
increases and thus by increasing the operating cost of vehicles. Fuel cost in the
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congested condition is estimated by the extra fuel consumed during congested period,
number of vehicle and cost of fuel. The traffic regime consists of different vehicles and
having different vehicle characteristics. Hence, the complexity arises to calculate the
fuel cost of each vehicle in the road way. Therefore, the vehicle characteristics such as
type and make of vehicle are to be noted down while taking the data collection for the
fuel cost. The cost of fuel may vary for a period of time. Hence, cost of fuel variation
taken into account while forecasting fuel consumption cost.
Indirect method describes a different fuel consumption model based on the empirical
data set. This estimation considered the factors affecting fuel consumption such as
vehicle parameters, driving condition and pavement conditions. A challenges in the
quantification of the fuel consumption using empirical method is that whether it
represents the congestion adequately. A drawback for considering the average speed to
predict congestion is that, the same average speed result in both peaks and off peak
periods.
The different Pollutant emits form the vehicle causes air pollution in the area. Traffic
congestion causes the vehicle emission. The emission cost is estimated by the emission
factor of pollutant per unit weight, length of road section travelled and cost of pollutant.
Direct and indirect method gives the emission rate of pollutant from vehicle. While
taking the measurements from the direct method, the aggressiveness of the driver has to
be tackled since the roadway accommodates different drivers behaving in a different
manner.
In indirect method, by use of the indicators of congestion such as average speed, idling
of vehicles can take to find the emission factors of each pollutant. This may cause bias
estimates because it represents a vehicle emission and a driver behavior. In order to
overcome that research has also to be focused on the driver behavioral and vehicular
characteristics of emission while measuring through direct method to get accurate data
for congestion.
Conclusion
This study presents a review of the different traffic congestion component, their
economic evaluation, and the challenges associated with quantifying each component.
This review highlights the various methods proposed by different researchers.
Therefore, in order to assess the overall economic impact of traffic congestion, it is
essential to take into account of all the relevant components and their economic value.
Even though, there is a considerable uncertainty in quantifying the impact of congestion
in economic terms, this paper provides an insight to do that.
Acknowledgement
The authors sincerely thank the support received from the Centre for Transportation
Research, Department of Civil Engineering, National Institute of Technology Calicut, a
Centre of Excellence setup under FAST Scheme of MHRD, Govt. of India.
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References
Arnott, R. (2007). Congestion tolling with agglomeration externalities. Journal of
Urban Economics, 62(2), 187–203. http://doi.org/10.1016/j.jue.2007.03.005
Beshears, J., Choi, J. J., Laibson, D., & Madrian, B. C. (2008). How are preferences
revealed? Journal of Public Economics, 92(8–9), 1787–1794.
http://doi.org/10.1016/j.jpubeco.2008.04.010
Bigazzi, A. Y., & Figliozzi, M. A. (2013). Marginal costs of freeway traffic congestion
with on-road pollution exposure externality. Transportation Research Part A:
Policy and Practice, 57, 12–24. http://doi.org/10.1016/j.tra.2013.09.008
Bilbao-Ubillos, J. (2008). The costs of urban congestion: Estimation of welfare losses
arising from congestion on cross-town link roads. Transportation Research Part A:
Policy and Practice, 42(8), 1098–1108. http://doi.org/10.1016/j.tra.2008.03.015
Boriboonsomsin, K., & Barth, M. (2010). Impacts of Road Grade on Fuel Consumption
and Carbon Dioxide Emissions Evidenced by Use of Advanced Navigation
Systems. Transportation Research Record: Journal of the Transportation Research
Board, 2139(1), 21–30.
Boter, J., Rouwendal, J., & Wedel, M. (2005). Employing travel time to compare the
value of competing cultural organizations. Journal of Cultural Economics, 29(1),
19–33. http://doi.org/10.1007/s10824-005-5796-2
Calfee, J., & Winston, C. (1998). The value of automobile travel time: implications for
congestion policy. Journal of Public Economics, 69(1), 83–102.
http://doi.org/10.1016/S0047-2727(97)00095-9
Cambridge Systematics, I., Dowling Associates, I., System Metrics Groups, I., &
Institute, T. T. (2008). NCHRP Report 618: Cost-Effective Performance Measures
for Travel Time, Delay, Variation, and Reliability. Planning And Administration.
Chesher, A., & Harrison, R. (1987). Vehicle operating costs: evidence from developing
countries. Osti.gov. Retrieved from
http://www.osti.gov/energycitations/product.biblio.jsp?osti_id=6844316
Errampalli, M., Senathipathi, V., & Thamban, D. (2015). Effect of congestion on fuel
cost and travel time cost on multi-lane highways in india,5(4), 458–472.
European Commission. (2004). Reclaiming city streets for people: chaos or quality of
life, 52.
Fezzi, C., Bateman, I. J., & Ferrini, S. (2014). Using revealed preferences to estimate
the value of travel time to recreation sites. Journal of Environmental Economics
and Management, 67(1), 58–70. http://doi.org/10.1016/j.jeem.2013.10.003
Franco, V., Kousoulidou, M., Muntean, M., Ntziachristos, L., Hausberger, S., & Dilara,
P. (2013). Road vehicle emission factors development: A review. Atmospheric
Environment. http://doi.org/10.1016/j.atmosenv.2013.01.006
Gaines, L. L., Hartman, C.-J. B., & Solomon, M. (2009). Energy Use and Emissions of
Idling-Reduction Options for Heavy-Duty Diesel Trucks. Transportation Research
Record: Journal of the Transportation Research Board, 2123(1), 8–16.
Goodwin, P. (2004). The Economic Costs of Road Traffic Congestion. ESRC Transport
Studies Unit University College London, (May).
Hall, F. L. (1992). Traffic stream characteristics. In Revised Monograph on Traffic Flow
Theory (Vol. 165, p. 2.1-2.36). Retrieved from
https://www.fhwa.dot.gov/publications/research/operations/tft/
Hansen, I. (2001). Determination and Evaluation of Traffic Congestion Costs. European
European Transport \ Trasporti Europei (2018) Issue 68, Paper n° 5, ISSN 1825-3997
18
Journal of Transport and Infrastructure Research, 1(1), 61–72.
Harford, J. D. (2006). Congestion, pollution, and benefit-to-cost ratios of US public
transit systems. Transportation Research Part D: Transport and Environment,
11(1), 45–58. http://doi.org/10.1016/j.trd.2005.09.001
Jiang, M., & Morikawa, T. (2004a). Theoretical analysis on the variation of value of
travel time savings. Transportation Research Part A: Policy and Practice, 38(8),
551–571. http://doi.org/10.1016/j.tra.2003.11.004
Jiang, M., & Morikawa, T. (2004b). Theoretical analysis on the variation of value of
travel time savings. Transportation Research Part A, 38(8), 551–571.
http://doi.org/10.1016/j.tra.2003.11.004
Khan, A. S., & Clark, N. (2010). An empirical approach in determining the effect of
road grade on fuel consumption from transit buses. SAE International Journal of
Commercial Vehicles, 3(1), 164–180. Retrieved from
http://www.scopus.com/inward/record.url?eid=2-s2.0-
79959493191&partnerID=40&md5=846c01914b2727da0b80b966cb08a106
Khan, T., & Mcips, R. I. (2013). Estimating Costs of Traffic Congestion in Dhaka City.
International Journal of Engineering Science and Innovative Technology (IJESIT),
2(3), 281–289.
Litman, T. (2013). Smart Congestion Relief. Victoria Transport Policy Institute, P12-
5310(April 2014), 3–40.
Liu, H., Barth, M., Scora, G., Davis, N., & Lents, J. (2010). Using Portable Emission
Measurement Systems for Transportation Emissions Studies. Transportation
Research Record: Journal of the Transportation Research Board, 2158(1), 54–60.
Lomax, T., Turner, S., Shunk, G., Levinson, H. S., Pratt, R. H., Bay, P. N., & Douglas,
G. B. (1997). Quantifying Congestion - Volume 1: Final Report.pdf. NCHRP
Report.
LUO, Q., JUAN, Z., SUN, B., & JIA, H. (2007). Method Research on Measuring the
External Costs of Urban Traffic Congestion. Journal of Transportation Systems
Engineering and Information Technology, 7(5), 9–12.
http://doi.org/10.1016/S1570-6672(07)60035-X
Ma, H., Xie, H., Huang, D., & Xiong, S. (2015). Effects of driving style on the fuel
consumption of city buses under different road conditions and vehicle masses.
Transportation Research Part D: Transport and Environment, 41, 205–216.
OECD Transport Research Centre. (2007). Managing traffic congestion managing
traffic congestion, ECMT European Conference of Ministers of Transport.
Managing.
Of, V., Causes, T. H. E., Well, A. S., & Possible, A. S. (2011). Traffic congestion in
Cairo, 3–5.
Rakha, H. A., Ahn, K., Moran, K., Saerens, B., & Bulck, E. Van den. (2011). Virginia
Tech Comprehensive Power-Based Fuel Consumption Model: Model development
and testing. Transportation Research Part D: Transport and Environment, 16(7),
492–503.
Román, C., Martín, J. C., Espino, R., Cherchi, E., Ortúzar, J. de D., Rizzi, L. I., …
Amador, F. J. (2014). Valuation of travel time savings for intercity travel: The
Madrid-Barcelona corridor. Transport Policy, 36, 105–117.
http://doi.org/10.1016/j.tranpol.2014.07.007
Rosenbloom, S. (1978). Peak-period traffic congestion: A state-of-the-art analysis and
evaluation of effective solutions. Transportation, 7(2), 167–191.
European Transport \ Trasporti Europei (2018) Issue 68, Paper n° 5, ISSN 1825-3997
19
Samuel, S., Morrey, D., Garner, C. P., Taylor, D. H. C., Fowkes, M., & Austin, L.
(2006). Deriving on-road spatial vehicle emission profiles from chassis
dynamometer experiments. Proceedings of the Institution of Mechanical
Engineers, Part D: Journal of Automobile Engineering, 220(1), 77–87.
Sarkar, D. (2012). Delay , fuel loss and noise pollution during idling of vehicles at
signalized intersection in Agartala city , India. Environmental Research, 2(6), 8–
15.
Shabbar, M., Adnan, M., Muhammad, S., & Abbas, S. F. (2014). Estimation of Traffic
Congestion Cost-A Case Study of a Major Arterial in Karachi. Procedia
Engineering, 77, 37–44. http://doi.org/10.1016/j.proeng.2014.07.030
Sierra, J. C. (2016). Estimating road transport fuel consumption in Ecuador. Energy
Policy, 92, 359–368.
Small, K. A. (2012). Valuation of travel time. Economics of Transportation, 1(1–2), 2–
14. http://doi.org/10.1016/j.ecotra.2012.09.002
The Economic Cost of Traffic Congestion in Florida. (2010), 19(August).
Thomson, I., & Bull, A. (2002). Urban traffic congestion: its economic and social
causes and consequences. CEPAL Review 76, 105–116.
Tong, H. Y., Hung, W. T., & Cheung, C. S. (2000). On-road Motor Vehicle Emissions
and Fuel Consumption in Urban Driving Conditions. Journal of the Air & Waste
Management Association, 50(4), 543–554.
http://doi.org/10.1080/10473289.2000.10464041
Tseng, Y.-Y., & Verhoef, E. T. (2008). Value of time by time of day: A stated-preference
study. Transportation Research Part B: Methodological, 42(7–8), 607–618.
http://doi.org/10.1016/j.trb.2007.12.001
Wang, H., Fu, L., Zhou, Y., & Li, H. (2008). Modelling of the fuel consumption for
passenger cars regarding driving characteristics. Transportation Research Part D:
Transport and Environment, 13(7), 479–482.
Weisbrod, G., Vary, D., & Treyz, G. (2001). Economic Implications of Congestion.
National Cooperative Highway Research Program. http://doi.org/ISSN 0077-5614
ISBN 0-309-06717-0
Wyatt, D. W., Li, H., & Tate, J. E. (2014). The impact of road grade on carbon dioxide
(CO2) emission of a passenger vehicle in real-world driving. Transportation
Research Part D: Transport and Environment, 32, 160–170.
Zhang, K., Batterman, S., & Dion, F. (2011). Vehicle emissions in congestion:
Comparison of work zone, rush hour and free-flow conditions. Atmospheric
Environment, 45(11), 1929–1939.
... d emissions and applying instantaneous fuel consumption model. They found that traffic congestion typically leads to an increase in fuel consumption of 80% and the influence of congestions on fuel consumption is distinctly lower than that on travel time. However, their study applies only to developing countries(Treiber, Kesting, and Thiemann 2008).Karuppanagounder and Krishnamurthy (2020) studied Economic Impact of Traffic Congestion Estimation and Challenges and three important impacts of traffic congestion namely delay impact, fuel consumption impact, and emission impact are considered.Primarily, the discussion of the congestion impact on traffic delay is considered and delay cost is estimated using the value of travel ...
... Secondly, the fuel consumption cost and emission cost are estimated by direct and indirect methods. Nevertheless, this research is not applicable all over the country(Karuppanagounder 2020).Teagan, Bentley, and Barnett (1998) studied cost reductions of fuel cells for transport applications and proposed the factors such as fuel choice, operating temperature, material selection, catalyst requirements, and controls on the cost of fuel processing systems, etc.There are fuel processor technology paths which manufacturing cost analyses indicate are consistent with fuel processor subsystem costs of under $150/kW in stationary applications and $30/kW in transport applications. Nevertheless, their study does not apply to developing countries(Teagan, Bentley, and Barnett 1998).Maibach et al (2001) studied on estimation of external costs in the transport sector andproposed that external cost includes the accident cost, fuel cost, air pollution cost, congestion/delay cost. ...
Thesis
A level crossing is a place where at least a railway line is crossed by a road on the level. Bangladesh is a developing country and traffic congestion at level crossing is a major problem for our country. There are many authorized and unauthorized level crossing in Gazipur. Among all crossings, several crossings are associated with major roads and several crossings are associated with minor roads. All level crossings are operated manually in our country. Most of the peoples those are involved in level crossing operations are not well trained. Traffic congestion at level crossing depends on several factors such as train speed, gate operator, traffic conditions on road, etc. Increasing traffic congestion at level crossing affects economic activities and finally affects national income. The congestion causes an annual loss between 20,000 crores and Tk 55,000 crore, says ARI (Accident Research Institute, BUET). The aim of this research to evaluate economic loss at important level crossing in Gazipur. Among all level crossing in Gazipur, we considered 5 major level crossing which are associated with significant roads. All data were collected by field and questionnaire survey during day and night. Delay cost, fuel cost, and emission cost are calculated by empirical formula. Total delay cost is 4.143492 Million USD in one year at 5 level-crossing for 10 trains only. Delay cost is highest at Joydebpur railway crossing with value of 1.548408 Million USD and lowest at Tongi railway crossing with value 0.148997 Million USD. The position of Joydebpur railway crossing in the 1st rank according to analysis of delay cost. However, all crossing has more or less delay cost and this delay cost has great impact on economic conditions of our country. Fuel cost analysis has shown that total fuel cost is 0.049583 Million USD in one year for 10 train at 5 major crossings in Gazipur. Individual fuel cost is highest for Mirer Bazar railway crossing and lowest for Tongi railway crossing. Mirer Bazar railway crossing, Joydebpur railway crossing, and Dhirasram railway crossing are in 1st, 2nd, and 3rd rank according to analysis, and Tongi railway crossing is in 5th rank. Evaluation of emission cost data total emission cost has been found 0.019127 Million USD in one year for 10 train at all considered level crossing. Emission cost is highest at Mirer Bazar railway crossing with numerical value of 0.006140 Million USD in one year and lowest in Tongi railway crossing with numerical value of 0.000462 Million USD in one year. Overall analysis has revealed that total cost is 4.212202 Million USD in one year due to delay, fuel consumption, and emission from vehicle during blockage time for 10 train at 5 major levelcrossing in Gazipur. Cost evaluation has shown that individual cost is highest at Joydebpur railway crossing and lowest at Tongi railway crossing. Joydebpur railway crossing, Mirer Bazar railway crossing, and Dhirasram railway crossing, are in 1, 2, and 3 ranked according to evaluated results. Finally, the study puts forward suitable recommendations from the findings of the study to minimize the delay cost, fuel consumption cost, and Emission cost and guides towards a suitable solution.
... In the process of urbanization and the rapid popularization of private vehicles, the problem of urban traffic congestion has become more and more prominent and produced numerous negative impacts on economy [1], [2] and environment [3], [4]. Traffic congestion can destroy the urban environment and ecology. ...
... Then, the traffic network dynamics (13) is BIBO stable. Per traffic dynamics (13), we can define [1] . . . ...
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This paper develops a conservation-based approach to model traffic dynamics and alleviate traffic congestion in a network of interconnected roads (NOIR). We generate a NOIR by using the Simulation of Urban Mobility (SUMO) software based on the real street map of Philadelphia Center City. The NOIR is then represented by a directed graph with nodes identifying distinct streets in the Center City area. By classifying the streets as inlets, outlets, and interior nodes, the model predictive control (MPC) method is applied to alleviate the network traffic congestion by optimizing the traffic inflow and outflow across the boundary of the NOIR with consideration of the inner traffic dynamics as a stochastic process. The proposed boundary control problem is defined as a quadratic programming problem with constraints imposing the feasibility of traffic coordination, and a cost function defined based on the traffic density across the NOIR.
... The cost of delayed travel time is the most commonly used factor to evaluate the economic impacts of traffic congestion. The most common method for estimating the cost is to apply the Value of Time (VOT) to estimate the delay costs [32]. Using Eq. (7), the travel time costs were estimated. ...
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Traffic congestion is one of the major barriers to the economic development of developing economies, resulting in severe social and economic impacts. The severity of traffic congestion in port and industrial areas is more thought-provoking than destructive barriers. The purpose of this research is to better understand the social, economic, and environmental effects of road traffic congestion in a developing city's port and industrial areas. This study adopted an on-site survey strategy to collect data from regular road-users by administering questionnaires , performing volume count surveys, and measuring travel time, delay time, and vehicle speed throughout the day. Along with documenting numerous social impacts, this assessment also assessed the impacts on stress through a four-point Likert-type scale. The congestion level in the four divided road sections was identified using the Level of Service index and based on respondents' opinions. The delayed costs, fuel loss costs, pollution costs, and loss of vehicle operators due to traffic congestion have been estimated. The study found that excessive vehicles, narrow roads, ineffective public transport modes, and bus operators' attitudes are causing congestion, resulting in high to extreme stress levels for road users, with a total economic loss of $2.01 million per day. The overall stress score of 3.23 ± 0.71 indicates the impact of traffic congestion on the respondents was high to an extreme level and had significant social impacts on different sociodemographic groups. This study can guide future work regarding sustainable transportation systems in emerging cities as well as traffic planning and policy adaptation for sustainable transportation systems, focusing on the reduction of adverse socioeconomic impacts.
... In other words, congestion happens when the number of vehicles present on a particular road at the same time exceeds its capacity. Generally, traffic congestion has negative effects on the economy because of time wastage, but it also has a negative effect on fuel consumption -which in turn causes gas emission and air pollution, noise pollution, and finally on vehicle wear and tear and on road safety (C P and Karuppanagounder, 2018). The delays caused by congestion make drivers waste a lot of time leading to late arrivals to work and possibly missing important meetings; and more importantly delays in delivery of goods often causing customer dissatisfaction. ...
... One of the major reasons behind traffic congestion is the inefficient use of the urban traffic networks. Traffic congestion continues to have a significant negative impact on the economy [1], and the environment [2] [3] due to increase in vehicle emissions, degrading air quality and posing significant health risks [4] [5]. Focused on improving mobility, saving energy, understanding and influencing travel behavior, traffic control is a significant and active research area in the field of Intelligent Transportation Systems (ITS). ...
Preprint
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Traffic congestion has become a nightmare to modern life in metropolitan cities. On average, a driver spending X hours a year stuck in traffic is one of most common sentences we often read regarding traffic congestion. Our aim in this article is to provide a method to control this seemingly ever-growing problem of traffic congestion. We model traffic dynamics using a continuous-time mass-flow conservation law, and apply optimal control techniques to control traffic congestion. First, we apply the mass-flow conservation law to specify traffic feasibility and present continuous-time dynamics for modeling traffic as a network problem by defining a network of interconnected roads (NOIR). The traffic congestion control is formulated as a boundary control problem and we use the concept of statetransition matrix to help with the optimization of boundary flow by solving a constrained optimal control problem using quadratic programming. Finally, we show that the proposed algorithm is successful by simulating on a NOIR.
... Over the last few years, researchers and regulators have been working together to reduce the impact of transportation sector on air pollution by implementing emission norms and recommending to adopt pollution control technologies; nonetheless, traffic emissions have not been significantly reduced due to increasing vehicular population and subsequent contribution to road dust emissions (Gulia et al. 2019;Shiva Nagendra et al. 2020). The tremendous growth of heavy-duty transport fleet (buses) on urban roads in recent years has led to increased traffic congestion and emissions (Gope et al. 2018;Karuppanagounder 2020;Suryawanshi et al. 2016;Wu et al. 2020). Furthermore, in developing countries like India, traffic characteristics are generally heterogeneous in nature (comprising different types of vehicles), resulting in a significant contribution to atmospheric pollution (Jaikumar et al. 2017;Kanagaraj et al. 2015). ...
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The present study examines the impact of urban attributes such as heterogeneous traffic, land use activities and meteorological factors on road dust resuspension and ambient particulate matter (PM) concentrations at urban roads. Silt load (SL) and PM emission rates from urban roads were quantified for 18 locations in Chennai and compared with 10 locations in Delhi city. Further, a detailed analysis was carried out for Chennai city alone to understand the size distribution of road dust, seasonal variation of SL, ambient PM concentrations and meteorological factors. Results indicated that the highest silt load values were observed on roads close to construction sites (Chennai = 34 g/m2 day and Delhi = 40 g/m2 day). Further, seasonal variation observations depicted that water stagnation on roads and poor drainage conditions during monsoon led to high silt load concentrations and subsequent PM emissions during post-monsoon season. Due to fugitive emissions and heavy-duty commercial vehicular (HCV) movement at construction sites, the 24-h average PM concentrations and total mass exposures (MET) were found to be highest compared to other locations (PM10 = 183.3 ± 25 μg/m3, PM2.5 = 80.7 ± 6.7 μg/m3 and MET = 2.66 μg/min). The PM2.5 and PM10 emission rates were also found to be higher at construction sites (PM2.5 = 6.65 and PM10 = 27.525 g/VKT) followed by the dumpsite (PM2.5 = 6.52 and PM10 = 26.96 g/VKT). The higher proportion of HCVs at dumpsite has led to higher PM emission rates. Further, the size distribution analysis of silt load showed that on average, 13–18% of dust deposited on roads is finer than 10 microns, 6–9% dust is finer than 2.5 microns and 4–6% of particulates is finer than 1 micron. The observations of the present study imply that traffic, land use and seasonality have a significant impact on road dust deposition and resuspension.
... In other words, congestion happens when the number of vehicles present on a particular road at the same time exceeds its capacity. Generally, traffic congestion has negative effects on the economy because of time wastage, but it also has a negative effect on fuel consumption -which in turn causes gas emission and air pollution, noise pollution, and finally on vehicle wear and tear and on road safety (C P and Karuppanagounder, 2018). The delays caused by congestion make drivers waste a lot of time leading to late arrivals to work and possibly missing important meetings; and more importantly delays in delivery of goods often causing customer dissatisfaction. ...
... During the urbanization process, numerous negative impacts have been created by the traffic congestion, such as environmental pollution [1], economic recession [2], [3], human physical and mental health harms [4], and ecological destruction [5]. Considering the acceleration of the urbanization process, it is urgent to solve the traffic congestion problem. ...
Preprint
Full-text available
In this paper, we introduce a new conservation-based approach to model traffic dynamics, and apply the model predictive control (MPC) approach to control the boundary traffic inflow and outflow, so that the traffic congestion is reduced. We establish an interface between the Simulation of Urban Mobility (SUMO) software and MATLAB to define a network of interconnected roads (NOIR) as a directed graph, and present traffic congestion management as a network control problem. By formally specifying the traffic feasibility conditions, and using the linear temporal logic, we present the proposed MPC-based boundary control problem as a quadratic programming with linear equality and inequality constraints. The success of the proposed traffic boundary control is demonstrated by simulation of traffic congestion control in Center City Philadelphia.
... In the process of urbanization and the rapid popularization of private vehicles, the problem of urban traffic congestion has become more and more prominent and produced numerous negative impacts on economy [1], [2] and environment [3], [4]. Traffic congestion can destroy the urban environment and ecology. ...
Preprint
Full-text available
This paper develops a conservation-based approach to model traffic dynamics and alleviate traffic congestion in a network of interconnected roads (NOIR). We generate an NOIR by using the Simulation of Urban Mobility (SUMO) software based on the real street map of Philadelphia Center City. The NOIR is then represented by a directed graph with nodes identifying distinct streets in the Center City area. By classifying the streets as inlets, outlets, and interior nodes, the model predictive control (MPC) method is applied to {\color{black}alleviate the network traffic congestion by optimizing the traffic inflow and outflow across the boundary of the NOIR.} The proposed boundary control problem is {\color{black}defined as a quadratic programming problem} with constraints imposing the feasibility of traffic coordination, and a cost function defined based on the traffic density across the NOIR.
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Impact of the odd–even (O/E) car rationing scheme implemented in Delhi city was assessed in this study. Traffic data, ambient air quality, vehicle speed and tailpipe emissions were measured along chosen urban corridors (UC) of the central business area (UC1), institutional area (UC2), and residential area (UC3), during and post-odd–even policy implementation. The intervention resulted in reduction of peak hour traffic flow by 27.6%, 25% and 25% at UC1, UC2 and UC3, respectively. The average emissions per trip decreased at all the UCs due to increase in the average speed and subsequent reduced idling time. The ambient Particulate Matter (PM) concentration at institutional area (UC2) was found to be lower during the O/E scheme as compared to post-O/E results; however, the PM values were observed to be higher in the other two locations. The increase in ambient PM concentrations at UC1 and UC3 can be attributed to a proportionate increase in non-exhaust emissions during the O/E scheme. The findings of present study reveal that the intervention had a positive impact on tailpipe emissions but a negative influence on non-exhaust emissions, as resuspension of road dust was contributing significantly ambient PM concentrations in Delhi during the O/E policy. Field observations revealed that interventions like odd–even cannot be successfully implemented and executed without people's engagement and participation.
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The vehicles normally move at their free speeds when it is least impeded due to traffic flow under lean traffic (free flow) conditions. As traffic flow increases, the vehicles cannot sustain their free speeds due to interactions from other vehicles in the traffic stream. In addition to that the vehicles that are operating in the congested traffic conditions will consume more fuel than those operating in steady state traffic conditions for the same average speed. This leads to increase in travel time and fuel consumption of the vehicles and thereby adding to total road user cost (RUC). On the contrary, fuel consumption is also high at very high speeds under free flow traffic flow conditions leading to increase RUC. Considering these scenarios, the travel time and fuel cost of the vehicle due to the congestion and free flow conditions (uncongested) has to be necessarily modelled in order to estimate realistic assessment of RUC on Indian highways. In the present study, the congestion cost relationships have been developed between Congestion Factor, a ratio of cost under congestion and steady state conditions and Volume-Capacity Ratio by considering various vehicle types plying on varying widths of multi-lane highways (four, six and eight lane divided carriageways) through the collection of exhaustive time related and fuel related data. Time related data was collected through questionnaire survey method whereas fuel consumption data was collected using advanced sophisticated fuel flow measuring equipment (V-Box). The developed equations have been successfully applied to demonstrate their applicability in terms of estimating realistic effect of congestion on time and fuel cost by considering a section on NH-2 in Delhi. The analysis shows that the congestion effect is more significant on fuel cost for heavy commercial vehicles whereas it is more prominent on time cost for passenger vehicles. However, the congestion effect on combined fuel and time cost is more significant on multi-axle trucks followed by cars, two wheelers and buses.
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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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