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Greenhouse gas emitted by the transport sector around the world is a serious issue of concern. To minimize such emission the automobile engineers have been working relentlessly. Researchers have been trying hard to switch fossil fuel to alternative fuels and attempting to various driving strategies to make traffic flow smooth and to reduce traffic congestion and emission of greenhouse gas. Automobile emits a massive amount of pollutants such as Carbon Monoxide (CO), hydrocarbons (HC), carbon dioxide (CO2), particulate matter (PM), and oxides of nitrogen (NO x). Intelligent transport system (ITS) technologies can be implemented to lower pollutant emissions and reduction of fuel consumption. This paper investigates the ITS techniques and technologies for the reduction of fuel consumption and minimization of the exhaust pollutant. It highlights the environmental impact of the ITS application to provide the state-of-art green solution. A case study also advocates that ITS technology reduces fuel consumption and exhaust pollutant in the urban environment.
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Research Article
Reduction of Fuel Consumption and Exhaust Pollutant
Using Intelligent Transport Systems
Mostofa Kamal Nasir,
Rafidah Md Noor,
M. A. Kalam,
and B. M. Masum
Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
Centre for Energy Sciences, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
Correspondence should be addressed to M. A. Kalam;
Received  February ; Accepted April ; Published  June 
Academic Editor: Mario L. Ferrari
Copyright ©  Mostofa Kamal Nasir et al. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
Greenhouse gas emitted by the transport sector around the world is a serious issue of concern. To minimize such emission the
automobile engineers have been working relentlessly. Researchers have been trying hard to switch fossil fuel to alternative fuels and
attempting to various driving strategies to make trac ow smooth and to reduce trac congestion and emission of greenhouse
gas. Automobile emits a massive amount of pollutants such as Carbon Monoxide (CO), hydrocarbons (HC), carbon dioxide (CO
particulate matter (PM), and oxides of nitrogen (NO
). Intelligent transport system (ITS) technologies can be implemented to
lower pollutant emissions and reduction of fuel consumption. is paper investigates the ITS techniques and technologies for
the reduction of fuel consumption and minimization of the exhaust pollutant. It highlights the environmental impact of the ITS
application to provide the state-of-art green solution. A case study also advocates that ITS technology reduces fuel consumption
and exhaust pollutant in the urban environment.
1. Introduction
Nowadays the energy saving issue is becoming more popular
in ITS. Recent increases in fuel prices have a great impact
on global economic changes. e drivers are worried about
their fuel consumption according to their monthly budget.
Excessive use of petroleum not only increases the budget but
also emits more pollutants []. e Texas A & M Transporta-
tion Institute found that due to congestion, urban Americans
have to travel . billion hours more and they are required to
purchase an extra . billion gallons of fuel for a congestion
cost of  billion while  billion pounds of additional
Carbon Monoxide (CO) and greenhouse gas released into the
atmosphere during urban congested conditions only in .
e world now suers heavily from environmental pollution
[, ]. Hence the reduction of fuel consumption can mini-
mize the pollutant emission and preserve the environment
clean and green []. ough signicant research has been
done by many researchers in the eld of fuel and energy
for alternative fuels, the vehicle industry also made some
attempts to improve vehicle modernization for fuel eciency
and economically viable environment friendly technology
[, ].
ITS can be dened as wire and wireless communications
with transportation system and vehicles [, ]. It is a modern
technique for the green technology that not only makes
a single vehicle green but also makes whole groups of
vehicles green. ITS is already revolutionized in the eld of
transportation systems [, ]. ITS covers a wide variety
of techniques and technologies such as real-time trac
information systems (TIS), electronic toll collection system
(ETCS), and automated trac light control system (ATLCS).
It is likely to emerge as the major tool to solve surface
transportation challenges over the next several decades, as
an infrastructure gets built alongside physical transporta-
tion infrastructure. is system deploys communications,
control, electronics, and computer technologies to improve
the performance of road transportation systems []. ITS
technologies are not visionary or futuristic; they are real,
already exist in several countries today, and are available to all
countries that focus on developing and deploying them. ITS
Hindawi Publishing Corporation
e Scientific World Journal
Volume 2014, Article ID 836375, 13 pages
e Scientic World Journal
consumption and exhaust pollutant which in terms protect
the environment []. e technologies alleviate congestion,
provide advanced safety, and enhance productivity []. ITS
application is used to minimize average distance, travel time,
and trac density estimation [].Itcanbeusedforgreen
purposes by informing the driver of the best path that can
reduce the signicant amount of fuel as the vehicle choice is
the less congested route [].
Vehicles can send and receive message with important
and direction []. An intelligent vehicle collects data using
some special sensors. Aer processing this data, it broadcasts
the information to other vehicles. Majority of vehicles in
present days run on fossil fuels [, ]. Hence, signicant
improvement is necessary for ITS to reduce fuel consumption
as well as pollutants which in terms prevents the global
warming and greenhouse gas []. e ITS technologies
promote the reduction of fuel consumption with two aspects,
that is, rst to reduce congestion that maintains each vehicle
to optimal speeds and secondly to give a suggestion to the
driver for a green fuel ecient path [].
is paper survey is to nd out the eect of ITS tech-
niques and technologies on energy saving and reduction of
environmental pollution from vehicles and road transporta-
tion systems including VV and VI, a green navigation sys-
tem which helps to nd out the best path for the minimization
of fuel consumption and exhaust pollutant to provide the-
the issues.
2. Literature Review
2.1. ITS Technology. ere are a number of techniques and
make the environment greener. ITS could be used for reduc-
tion of fuel consumption which would make the environment
clean and green []. Table showsmanytechniquesand
technologies used for the reduction of fuel consumption
in the road transportation system. Fuel consumption can
be reduced by two ways, that is, reduction of fuel use and
minimization of the average distance. Secondly, the technique
on fuel consumption reduction introduces the importance
of reduction of fuel consumption for green driving and
reduction of fuel by intelligent driving, while minimization of
navigation and trac reduction by transportation reduction.
e ITS techniques and technologies can facilitate the reduc-
tion of fuel consumption by improving the driving behavior
and minimizing the trac congestion [].
e ITS techniques and technologies can reduce energy
consumption by changing the driving behavior, suggesting
congestion free smooth path, automatic trac control signal,
electronic toll collection, and platooning. From the mechan-
ical properties of the vehicle the automobile engineer proved
that the vehicle running – km/h for gasoline engines
and – km/h for the petrol engine consumed the lowest
rate of fuel. Figure illustrates the basic relationship of the
vehicle speeds with the fuel consumption from which exhaust
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
Speed (km/h)
Electric and
hybrid car
Green speed
Fuel consumption (L/100 km)
F : Relation between fuel consumption and average speed.
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140
Speed (km/h)
1st gear
2nd gear
3rd gear
4th gear
5th gear
6th gear
Fuel consumption (L/100 km)
F : Relation between fuel consumption and gear change of a
manual driving car.
pollutantbythedrivingpatterncanbeassumed[, ]. By
eliminating the congestion and suggesting an uninterrupted
path with the aid of ITS technique the vehicle can maintain
this green speed and then obtain the best fuel eciency
and pollution at minimum level []. If the vehicle drives
above green speed or runs bellow the green speed it will
consumemorefuel[]. e curve C in Figure shows that
if the aerodynamic drag is reduced at high speed, then fuel
consumption will also be reduced []. e speed versus fuel
doted das line.
Figure shows how the fuel consumption varies accord-
maintain the engine in low speed and high torque mode is to
select the highest speed ratio. Engine consumes less fuel in
rd gear than in st gear and less fuel in th gear than in th
loaded. e manual transmission vehicle goes to the highest
speed ratio as soon as possible. When going up a slope, avoid
shiing to a lower gear as much as possible to keep engine
braking so as to recover energy over a greater distance. With
an automatic transmission, it is more dicult to control speed
ratios but this can be done by momentarily taking foot o
the gas pedal when going up a slope to reach the upper speed
e Scientic World Journal
T : Techniques and Technologies for fuel reduction of vehicle.
Reduction Type Attribute
Fuel Reduction
Importance of
Reduction of Fuel
Consumption for
Green Driving
Improvement of Fuel
Eciency of Vehicle By
Upgrading Mechanical
Improvement of Highways
Upgrading Civil
Reduction of Fuel
by Intelligent
Green Driving
Maintain Optimum Tire Pressure
Adjust Drive Technique
Maintain e Ride
Get Rid of Weight and Reduce the Drag
Avoid Unnecessary Idling
Use Latest Technology Car
Trac Flow
ment of
Electronic Toll Collection
Trac Trac Light Control
Collision Avoidance
Navigation System
Bottleneck Elimination
Electronic Toll
Trac Reduction
by Navigation
Occupancy Increase
Car Sharing, Car
Other Eective Factor
For Transportation
Trac Reduction
by Transportation
Minimization Of
Demand Management
Road Pricing
Parking Strategies
No Transportation
City Planning
Compact City
If automatic transmission vehicle has an optional speed
ratio, activate it to obtain a higher ratio, which will reduce
speed and fuel consumption. On a road with many ground
level dierences avoid using the speed regulator to maintain
a constant speed, as the gearbox will shi to a lower speed and
will increase the engine speed when going up a slope in order
to maintain the same speed []. Figure presents the vehicle
emission as function of average speed []. Figure (a) shows
that, at low speed, car emits the highest CO while higher
speed emits minimum pollutant. e greener speed range is
– km/h in terms of emission. At green speed, it emits
the lowest level of CO []. Figure (b) shows the emissions
of VOCs or HCs and NO
versus average speed. Masum et
al. []reportedthatNO
increases with engine speed as
more fuel is burnt resulting in high in-cylinder temperature
at high speeds. NO
emission increases more than linearly
with the increase of average speed [, ]. At lower speed
emission is lower but HC and CO emissions are higher.
Rich fuel-air mixture and incomplete combustion are the
reasons behind higher CO and HC emission at lower engine
speed. Few authors [, ] get higher CO and HC emission
at lower engine speed. At higher engine speed, CO and HC
emissions are also higher []. At higher engine speed, the
air-fuel mixture gets a shorter time to complete combustion
thatresultsinhigherHCandCOemissions[]. Finally we
can conclude by analyzing all those graphs that km/h
is the best average speed both in terms of energy eciency
and in terms of greener environment.
2.2. Fuel Saving ITS Application. AnumberofITSappli-
cations have to reduce the fuel consumption and exhaust
pollutant. e ITS related technologies are described below.
2.2.1. Intelligent Trac Signal Control. e ITSC system plays
an essential role in both safety and eciency of road trac
[]. e target of the ITSC system is the reduction of
congestion queue time in trac signal. ITSC reduces the
waiting time in trac control signal []. ITSC uses a wireless
communication between RSU and vehicle []. e eects of
ITSC are the reduction in congestion, the economic eect,
and the reduction of pollutant. Vehicles in a stop-and-go
running consume more fuel and emit more pollutants than
constant speed driving. Very low average speeds generally
represent stop-and-go driving and vehicles do not travel
e Scientic World Journal
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140
Emission rate (g/km)
Average speed (km/h)
Emission rate (g/km)
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140
Average speed (km/h)
F : Typical relation between emission and average speed. (a) CO versus average speed and (b) NO
and VOC versus average speed.
far. erefore, the emission rates per mile are quite high.
When a car’s engine is running but it is not moving, its
emission rate per mile reaches innity []. Vehicles need to
be smoothed for reducing CO
emissions by minimizing the
stop-and-go times. Wen [] proposed a three-tier dynamic
TLC system structure to minimize the emitted pollutant by
uninterrupted driving. Maslekar et al. [] proposed an ITLC
system which assumed that every vehicle will be equipped
with GPS, OBU, and navigation system. GPS devices collect
all the information about the vehicle and road present status.
OBU devices send information about the vehicle speed, accel-
eration, and direction by WAVE. e ETC center processes all
the information and reasoning by ITLC algorithm. e brief
description of three-tier open trac light control model []
is shown in Figure .
(i) Tier-: tier- is responsible for collecting trac infor-
mation, receiving light phase data, and sending trac
ow data and it also calculates the suggested speeds.
GPS devices will provide the vehicle state informa-
tion. To transmit the current trac information to
ITSC, vehicle uses the OBU devices. e OBU will
calculate the recommended speed when vehicles get
the trac information from the trac lights. By using
the ITSC the drivers may minimize the waiting time
and also minimize number of stops.
(ii) Tier-: tier- controls the receiving and saving of
trac ow data and sends the control result to the
ITSC from the OBUs. It has three parts, that is,
antennas, storage, and trac lights. e ETC’s OBU
devices antennas in tier- can communicate with
other devices by wireless communications; hence,
the trac light will receive the real-time trac ow
information. At the same time, the trac control
results will be sent to ECTs OBU and then drivers can
know the trac light phases in time. e purpose of
(iii) Tier-: data processing task is done in tier- from
the three sections. Data extraction is in Section .e
antenna periodically accepts the trac information
from the vehicles. Data processing task is done in
this tier and data is fed from the tier- of ITSC.
Road trac ow data is collected by ETC system and
recommends the best speed. An open interface for
third-party application is operated at Section .
2.2.2. Electronic Toll Collection Systems (ETCS). ETCS is a
system that permits for collection of toll payments and trac
monitoring electronically by uninterruptedly of vehicle mov-
ing []. ETCS have several parts for operating such as wire-
less communication, in-road/roadside sensors, electronic
tags, and vehicle equipped with onboard equipment. ETCS
provides general vehicle monitoring and data collection and
collects the tolls. ETCS operate while vehicles run at near-
highway cruising speed for collecting the tolls and increase
eciency and reduce congestion and travel time and reduce
pollution. ETCS makes the toll gates less congested and as
a result reduces the exhaust pollutant. e annual pollutant
emission will be reduced to half if the urban expressway
network uses ETCS. Figure shows a typical ETCS system.
is reduced at a signicant level. is analysis also showed that
the air pollution emission levels at the toll booth links are
reduced for all pollutants.
2.2.3. Trac Information System. TIS is very important
for ITS application. e information about the number of
vehicles in the road is very important to eliminate the trac
congestion. e trac information system gathers the trac
data and transmits this data to the driver in the roads [].
In VANET, every vehicle periodically exchanges information
every  ms. e trac density is the most inuential
factor that aects the average speed of the vehicle [, ].
ITS application performance depends on how accurately it
can measure the trac ow rate, trac density, and mean
speed of the vehicle. VANET is a high mobility network
e Scientic World Journal
ird party
Trac control center
Trac light control
emission calculation
Trac light/storage/antenna
Data collection
Trac flow collection
Recommended speed calculation
F : ree-tier open trac control system.
Vehicle gate
Vehicle height
Vehicle detector
F : Electronic toll collection system.
that greatly aects the green measures. Fuel consumption
varies due to dierent speeds, accelerations, stop-and-go
times, dierent followed routes, and the level of trac
2.2.4. Cooperative Driving. e cooperative driving is an
automatic driving of over or lanes used for openly lane
changing, merging, and splitting for congestion free driving.
e main aim of the cooperative driving is to save the energy
and to minimize the air pollution []. It is a vehicle-to-
vehicle based communication []. e system was tested
rst in  by the AETAT using the VV infrared signal
[]. e distance between the vehicles was measured using
triangulation between a pair of infrared markers on the top
of a preceding vehicle during cooperative driving. In the
cooperative driving application the requirement for the VV
communication is the compatibility of the real-time data
transmission required for automated driving.
2.2.5. Platooning. e platooning can be dened as a collec-
tion of vehicles that travel together and actively coordinate
information []. Platooning oers a list of advantages
including increase of fuel and trac eciency, safety, and
from the trac congestion by vehicle automation technol-
ogy. It operates each vehicle close together with compare
to manual driving condition; hence every lane can carry
approximately double the trac than current manual system.
is obviously shrinks the trac congestion in highway. It
maintains a close spacing aerodynamics drag that results in a
major reduction in fuel consumption and exhaust pollutant.
Result has shown how that drag reduction improves the
fuel eciency and emission reduction by  to %. For
e Scientic World Journal
T : Summary of ITS application.
Authors Application Technology Objectives
Fuyama []
Electronic toll collection
System (ETCS)
Wireless communication between a roadside
antenna in a tollgate and a vehicle unit in a
moving vehicle
Maintain a constant green speed
in toll gate
Tengler and He []
Vehicle Information
Systems (VICS)
Provide the trac and travel data to the drivers by
transmitting using wireless technology.
Reducing trac congestion,
trac accidents, and improving
road environment
Glass et al. []
Trac Management
Systems (TMS)
TMS include onboard satellite navigation devices
as well as dynamic driver assistance and variable
message signs.
Transport can be made safer,
cheaper, more reliable and
Boatright et al. []
Vehicle Navigation
System (VNS)
Uses information from a Global Positioning
System (GPS) to obtain velocity vectors, which
include speed and heading components.
Advice the driver for the shortest
and fuel ecient path.
Pfeier et al. []
Driver Assistance
Based on intelligent sensor technology constantly
monitor the vehicle surroundings as well as the
driving behavior.
Detect potentially dangerous
situations at an early stage and
actively support the driver
Hoeger et al. []
Automated Driving
Real-time driving functions necessary to drive a
ground-based vehicle without real-time input
from a human operator.
Trac-jam reduction and
full-range automated cruise
Masum et al. []
Urban Trac
Information Systems
Create, analyze and process the location
information of moving vehicle to improve
convenience by providing improved ow of
transportation logistics and analyzed trac
information to driver.
Total management system of the
streetlight light and security light
and reduction of pollution
Wiering et al. []
Intelligent Trac Light
Control System.
Intelligent trac light control system comprising
a microprocessor, a manual input device, an
enforced switching device and an intelligent
detecting device, where in the microprocessor is
used for controlling trac lights.
Maximize the trac eciency of
intersection of roads and
achieving a best control for
Lemelson and Pedersen
Vehicle Collision
Avoidance System
It uses radar and sometimes laser and camera
sensors to detect an imminent crash.
To reduce the severity of an
accident which in term reduce
de Fabritiiset al. []
Trac Estimation and
Prediction System
Use computer, communication, and control
technologies to monitor, manage, and control the
transportation system.
Improve trac conditions and
reduce travel delays.
Smith, et al. []
Scalable Urban Trac
e SURTRAC dynamically optimizes the control
of trac signals in three sections: rst, decision
making in decentralized manner of individual
intersections; second is an emphasis on real-time
responsiveness to changing trac condition and
nally managing urban road networks.
Objectives include less waiting,
reduced trac congestion,
shorter trips, and less pollution.
Blum et al. []
Intelligent Speed
Adaptation (ISA)
ere are four types of technology used for ISA:
GPS, Radio Beacons, Optical recognition, Dead
ISA helps to reduction of
accident risks and reductions of
noise and exhaust emissions.
continuing such as SARTRE [], a European platooning
project; PATH [], a California trac automation program
that includes platooning; GCDC [], a cooperative driving
initiative; SCANIA [] platooning and; Energy ITS [], a
Japanese truck platooning project.
e summary of the ITS applications is given in Table .
3. Proposed Fuel-Saving Navigation System
Design of dynamic green driving advisor should satisfy the
following goals and requirements.
(i) Use ITS techniques and technologies to gather the
real-time trac information and the green navigation
system will update the trac information to modify
the planned path adaptively.
(ii) Calculate accurately the vehicle ow rate based on the
trac ow theory.
(iii) To estimate the vehicle density on specic time use
historical trac information.
(iv) Try to maintain the average green speed (–
 km/h) to get fuel eciency as well as pollutant at
minimum level.
e Scientic World Journal
(v) Design of dynamic speed limit should satisfy the goals
and requirements of green driving.
(vi) e strategy should work even when only one vehicle
is doing green driving; more vehicles doing green
driving would smooth trac better.
3.1. Model Assumption. To achieve the objective behind
developing a fuel ecient route selection model, some
assumptions need to be agreed on to fulll the requirements.
For example, each vehicle is equipped with a set of devices,
which are considered to be available on the vehicles at
the present time. ese include the OBU, preloaded digital
road maps, GPS, and NS. Each vehicle equipped with OBU
system collects its own trac information, including location,
spacing, velocity, and acceleration, from GPS device [].
It is also able to communicate with other vehicles equipped
with IVC system by DSRC. Hence, vehicles in transportation
system can share their information based on this information;
drivers can decide their driving behaviors to smooth trac.
An ecient fuel saving navigation system estimates the green
optimum path []. A green navigation system provides
suggestion for fuel ecient route to driver based on available
information about fuel dependent parameter of each vehicle
for unraveling trac congestion. When a driver plans to go
on a destination, he sends a query to navigation server with
vehicle position and destination by ITS. e server will nd
the best fuel ecient paths to destination considering current
and historical trac data. In ITS technology, a number of
sensors are installed in the road section to nd out the vehicle
density, trac ow rate, and the vehicle mean speed. e next
section shows the mathematical model of how to calculate
those three, that is, the vehicle density, trac ow rate, and
the vehicle mean speed.
3.2. Vehicle Density. Vehicle density referred to the number
of vehicles per kilometer in a specic time. Vehicle density
measures the number of vehicles at location in certain
time interval and can be measured for a road section with
 length as
e vehicle density varies with location and time. So
considering those parameters in ()itcanbewrittenas
is the time interval
is the road section. Normally the unit of the vehicle
density is vehicles per kilometer. Now we can make a general
form by multiplying numerator and denominator of ()bya
small time interval dt. Consider
 
e numerator of ()isthetotalnumberofvehiclesin at
time and the denominator shows the area of the measure-
ment interval . So the vehicle density for a measurement
interval at location and at time canbewrittenas
Tota l Number of Vehicles in at Time
3.3. Vehicle Flow Rate. Vehicle ow rate is the numb er of
vehicles thatpass through a certain road section per time unit.
e vehicle ow rate Φ at location
and a time interval 
of measurement interval
can be dened as follows.
For a time interval  at any location
, the ow rate is
e number is the total number of vehicles that pass
through the location
during .eunitofvehicle
ow rate is vehicle per hour. Multiplying the numerator and
the denominator by a small location interval dx we nd
a more general form for vehicle ow rate. e numerator
becomes the total distance travelled by all vehicles and the
denominator is the area. Consider
 
Total Distance Covered by Vehicles in
Area 
From () we can nd the general denition for vehicle ow
rate as follows:
 
Total Distance Covered by Vehicles in
is the total distance covered by the vehicle.
e vehicle ow rate versus hour report provides a
graph report that shows the historical trac ow volumes
and average speed of the transportation network during a
selected time period of the day. is information is useful
for analyzing the historical performance of the transportation
network and implementing proactive measures to improve
the ow of trac and it is useful to make a decision for green
route selection. Figure showsatypicaltracowversus
time of day.
3.4. Vehicle Mean Speed. e vehicle mean speed can be
dened as the average speeds of all the vehicles for a location
in a certain interval. e vehicle mean speed also depends
on location, time, and measurement intervals. We can make
e Scientic World Journal
0 2 4 6 8 1012141618202224
Vehicles (vph)
Time of day
F : Typical trac ow versus time of day.
a relationship with vehicle density and vehicle ow rate as
Total distance covered by vehicles in
Total time spent by vehicles in
From ()wecanrewritethevehiclemeanspeedasthe
fundamental relation of trac ow theory as follows:
is is the general relation among vehicle ow rate, density,
and mean speed. Using this equation, by knowing two of these
variables, we can easily nd the third variable. e vehicle
mean speed for total vehicles in the interval at location
and point in time can be calculated as
From ()and() we can easily nd the mean speed
4. Methodology
e proposed green fuel ecient route choice procedure
uses dierent ITS technologies. e green navigation method
nds the multiple candidates for a specic journey and
chooses the most fuel ecient route. e method avoids
manual trac signal and toll collection and does not select
a route to a destination in which a trac jam might happen.
e most fuel ecient route between sources to destination
may be dierent from the shortest and fastest routes. ere
are several factors that aect the fuel consumption on streets.
ese parameters are classied into four categories, that
is, static street parameters, dynamic street parameters, car
specic parameters, and personal parameters. Static street
parameters model the street characteristics and do not change
(or change very infrequently) over a period of time. For
example, the speed limits of streets change very infrequently
or less constant. e dynamic street parameters are charac-
teristics that change with time. for example, the congestion
levels on a street or the average speed on a street. e static
and dynamic street parameters together determine the fuel
eciency of a particular street. Other variations in the fuel
consumption can occur due to the type of car being driven
and the nature of the persons driving. For example, a big
car may consume more fuel than a small car. Similarly a
person who is more erratic (higher acceleration or hard
braking) is likely to consume more fuel than a more careful”
driver. ese parameters account for the variation in fuel
consumption due to the car type and the driver behavior.
e proposed system is a linear model that can accurately
predict the fuel consumption across urban trac streets. We
will summarize this model below. e input to the model
(i) static street parameters: number of stop signs (ST)
from source to destination;
(ii) dynamic street parameters: V, V
,whereV is the
vehicle means speed on a specic street.
4.1. Mathematical Model. e mean speed can be obtained
from ().
Total fuel consumption that a vehicle consume in an
urban journey is fuel consume at while running and consume
at stop sign. Consider
Totalfuelconsumption= fuel consume at running
+ consume at stop sign.
e nal model is expressed as
Total fuel consumption TFC =
where TFC = Total fuel consumption,
= length of road
section i (
), V
= mean speed of road section
fuel consumption per second while vehicle at idle, and
idle time at point .
4.2. Material and Methods. As stated before, the shortest
path route or minimum travel time route may not always be
the fuel ecient path. Street congestion, elevation variability,
average speed, and average distance between stops (e.g., stop
signs) lead to changes in the amount of fuel consumed
making fuel ecient routes potentially dierent from the
shortest or fastest routes and a function of vehicle type. To
experiment and analyse the fuel saving model, a pair of
source destinations with multiple routes at Kuala Lumpur was
selected. Experiment was done in three dierent scenarios,
Figure shows three dierent routes from the source
point A to destination point B. e distance of route is
. km, route  is . km, and route  is . km. From
Figure itwasshownthat:pmto:amtheroadin
e Scientic World Journal
F : ree dierent routes of the same origin and destination.
Tot a l time
Fuel used
Route 1
Route 2
Route 3
F : Bar graph for the distance, total travel times, and fuel used
in free ow condition.
free ow condition. During : am to : pm of the day
is moderate congestion where as heavy congestion occur two
time slot of the day; rst one is morning oce time from :
am to : am and second one is : pm to : pm.
5. Result and Discussions
5.1. Free Flow Condition. Byillustratingthefreeowcondi-
tion, the shortest distance route is also fuel ecient and
also emits relatively lower pollutant. Table shows all the
data found in free ow condition in three dierent routes.
Figure shows the bar graph for the distance, total travel
5.2. Moderate Congestion. To demonstrate the moderate
congestion condition, Table shows the detailed data of this
at random manner. is time route performs the most fuel
Tot a l time
Fuel used
Route 1
Route 2
Route 3
F : Bar graph for the distance, total travel times, and fuel used
in moderate congestion.
Tot a l time
Fuel used
Route 1
Route 2
Route 3
F : Bar graph for the distance, total travel times, and fuel
used in heavy congestion.
ecient and environment friendly; it may dier from other
Figure shows the bar graph for the distance, total travel
times, and fuel used in average congestion in three dierent
5.3. Heavy Congestion. In a heavy congested condition the
road is very rushy as at morning most of the travelers go for
shows the details of the study; route is more fuel ecient
than the other two routes though route is the shortest route.
Figure  shows the bar graph for the distance, total travel
times, and fuel used in heavy congestion in three dierent
 e Scientic World Journal
T : Free ow Condition Fuel Consumption.
Performance Measure Route Route Route Remarks
Distance (Km) . . .
Running time (Minutes)  m  m  m
Stop time (Minutes) m m m
Total time (Minutes)  m  m  m
Total distance w.r.t. time  Km  Km  Km Assumption-
Fuel used (Liter) . . .
Fuel consumption (Lt/Km) . . .
T : Performance on moderate congestion road condition.
Performance Measure Route Route Route Remarks
Distance (Km) . . .
Running time (Minutes)  m m  m
Stop time (Minutes) m . m m
Total time (Minutes)  m . m  m
Total distance w.r.t. time  Km . Km  Km Assumption-
Fuel used (Liter) . . .
Fuel consumption (Lt/Km) . . .
T : Performance on heavy congested road condition.
Performance Measure Route Route Route Remarks
Distance (Km) . . .
Running Time (Minutes)  m  m  m
Stop Time (Minutes) m m m
Total time (Minutes)  m m  m
Total distance w.r.t. time  Km  Km  Km Assumption-
Fuel used (Liter) . . .
Fuel Consumption (Lt/Km) . . .
6. Conclusion
Green technology is one of the most important considera-
tions on developing ITS, foster environmental sustainability,
and the economics of energy eciency. e important issues
of green technologies are related to energy eciency in auto-
mobile industry and promote environment friendly commu-
nication technologies and systems. Green ITS technologies
play a signicant role in reducing energy consumption
applications. is paper provides a survey on the eects of
ITS related techniques on the reduction of fuel consumption
and exhaust pollutant. In ITS, most of the applications are
for highlighting trac safety and infotainment. However,
this research work sorts out ITS technologies that deploy
for fuel saving and green environment. Finally, this research
proposed a green navigation technology that used the current
trac ow data as well as historical trac information. A
case study shows that if the driver uses the green navigation
For short distance and single vehicle it shows a little impact,
but if it is considered for long distance and millions of vehicle
it will have signicant contribution in terms of energy and
AETAT: Association of electronic technology for
automobile trac
ADS: Automated driving system
ATLCS: Automated trac light control system
CMEM: Comprehensive modal emissions model
CO: Carbon monoxide
: Carbon dioxide
DAS: Driver assistance systems
DSRC: Dedicated short range communication
EPA: Environmental protection agency
ETCS: Electronic toll collection system
ETC: Electronic trac control
FTP: Federal test procedure
GHG: Greenhouse gas
GNS: Green navigation system
GPS: Global position system
e Scientic World Journal 
HC: Hydrocarbons
ISA: Intelligent speed adaptation
IT: Information technology
ITLCS: Intelligent trac light control system
ITS: Intelligent transport system
IVC: Intervehicle communication
LDT: Light duty truck
LSR: Least square regression
MEC: Modal emission cycle
MoE: Measures of eectiveness
: Oxides of nitrogen
NS: Navigation system
OBU: On-board unit
ORNL: Oak Ridge National laboratory
RSU: Road side unit
SURTRAC: Scalable urban trac control
TEPS: Trac estimation and prediction system
TIS: Trac information systems
TMS: Trac management systems
UTIS: Urban trac information systems
VI: Vehicle-to-infrastructure (VI)
VV: Vehicle-to-vehicle
VA NET: Vehi c u l ar ad hoc n e t wo r k
VCAS: Vehicle collision avoidance system
VICS: Vehicle information communication
VMT: Vehicle miles travelled
VNS: Vehicle navigation system
VOC: Volatile organic compounds
WAVE: Wireless access for vehicular environment.
Conflict of Interests
e authors declare that there is no conict of interests
regarding the publication of this paper.
e authors would like to thank the High Impact Research
of University of Malaya and Ministry of Higher Education of
Malaysia Project no. UM.C/HIR/MOHE/FCSIT/ for their
[] T. Mahlia, S. Tohno, and T. Tezuka, “International experience
on incentive program in support of fuel economy standards and
labelling for motor vehicle: a comprehensive review, Renewable
and Sustainable Energy Reviews,vol.,pp.,.
[] T.Mahlia,S.Tohno,andT.Tezuka,“Historyandcurrentstatus
of the motor vehicle energy labeling and its implementation
possibilities in Malaysia, Renewable and Sustainable Energy
[] M. N. Uddin, W. M. A. W. Daud, and H. F. Abbas, “Potential
hydrogen and non-condensable gases production from biomass
pyrolysis: insights into the process variables, Renewable and
Sustainable Energy Reviews,vol.,pp.,.
for transportation vehicles: a technical review, Renewable and
Sustainable Energy Reviews,vol.,pp.,.
[] E. Deakin, “Sustainable development and sustainable trans-
portation: strategies for economic prosperity, environmental
quality, and equity, Working Paper -, Institute of Urban
and Regional Development, University of California at Berke-
ley, Berkeley, Calif, USA, .
[] M. N. Uddin, W. M. A. W. Daud, H. F. Abbas, M. T. Islam, Z.
Z. Chowdhury, and S. Das, “Eects of pyrolysis parameters on
hydrogen formations from biomass, RSC Advances,vol.,no.
, pp. –, .
[] F.J.Martinez,C.-K.Toh,J.-C.Cano,C.T.Calafate,andP.Man-
zoni, “Emergency services in future intelligent transportation
systems based on vehicular communication networks, IEEE
Intelligent Transportation Systems Magazine,vol.,no.,pp.
, .
[] M. K. Nasir and M. Whaiduzzaman, “Use of cell phone density
for Intelligent Transportation System (ITS) in Bangladesh,
Jahangirnagar University Journal of Information Technology,vol.
[] W. Feng, H. Alshaer, and J. Elmirghani, “Green information and
communication technology: energy eciency in a motorway
model, IET Communications,vol.,no.,pp.,.
[] A. GhaarianHoseini, N. D. Dahlan, U. Berardi, A. Ghaarian-
Hoseini, and N. Makaremi, “e essence of future smart houses:
from embedding ICT to adapting to sustainability principles,
Renewable and Sustainable Energy Reviews,vol.,pp.,
[] M. Alsabaan, K. Naik, and A. Nayak, Applying vehicular ad
hoc networks for reduced vehicle fuel consumption, in Recent
Trends in Wireless and Mobile Networks,pp.,Springer,
New York, NY, USA, .
[] European Commission, ICT for the Fully Electric Vehicle,
Research Needs and Challenges Ahead, European Commision,
DG Information Society and Media, Directorate G “Compo-
nents and Systems Units G., .
[] M.A.ChowdhuryandA.W.Sadek,Its Fundamentals of Intelli-
gent Transportation Systems Planning,ArtechHouse,Norwood,
Mass, USA, .
[] J. Barrachina, P. Garrido, M. Fogue et al., “Reducing emergency
services arrival time by using vehicular communications and
Evolution Strategies, Expert Systems with Applications,vol.,
[] M. Alsabaan, W. Alasmary, A. Albasir, and K. Naik, “Vehicular
networks for a greener environment: a survey, IEEE Communi-
cations Surveys & Tutorials,vol.,no.,pp.,.
A survey and challenges in routing and data dissemination
in vehicular ad hoc networks, Wireless Communications and
Mobile Computing,vol.,no.,pp.,.
[] Y. S. Najjar, “Protection of the environment by using innovative
greening technologies in land transport, Renewable and Sus-
tainable Energy Reviews,vol.,pp.,.
A. Abraham, “Cooperative game theoretic approach using
fuzzy Q-learning for detecting and preventing intrusions in
wireless sensor networks, Engineering Applications of Articial
[] M. Ahmed, M. R. J. Sattari, M. K. Nasir et al., “Vehicle adhoc
sensor network framework to provide green communication for
 e Scientic World Journal
urban operation rescue, Lecture Notes on Information eory,
vol. , no. , pp. –, .
[] G. Karagiannis, O. Altintas, E. Ekici et al., “Vehicular net-
working: a survey and tutorial on requirements, architectures,
challenges, standards and solutions,IEEE Communications
Surveys & Tutorials,vol.,no.,pp.,.
[] H. Hartenstein and K. P. Laberteaux, A tutorial survey on
vehicular ad hoc networks, IEEE Communications Magazine,
[] S. Tsugawa and S. Kato, “Energy ITS: another application of
vehicular communications, IEEE Communications Magazine,
[] S. Fuyama, “Electronic toll collection system, Google Patents,
[] S. Tengler and R. He, “Vehicle information communication
system, Google Patents, .
J. Gibson, and D. L. Iverson, Automated trac management
system and method, Google Patents, .
[] R. Boatright, D. Olsen, and L. Pearson, “Vehicle navigation
system, WO Patent no. , .
uck, U. St
ahlin, S. L
uke, and M.
Komar, “Driver assistance systems, WO Patent no. ,
[] R. Hoeger, A. Amditis, M. Kunert et al., “Highly automated
vehicles for intelligent transport: have-it approach, in Proceed-
ings of the 15th World Congress on Intelligent Transport Systems
and ITS Americas Annual Meeting,pp.,NewYork,
NY, USA, November .
[] B. M. Masum, H. H. Masjuki, M. A. Kalam, I. M. Rizwanul
Fattah, S. M. Palash, and M. J. Abedin, “Eect of ethanol-
gasoline blend on NO
emission in SI engine, Renewable and
Sustainable Energy Reviews, vol. , pp. –, .
[] M. Wiering, J. van Veenen, J. Vreeken, and A. Koopman,
“Intelligent trac light control, Tech. Rep. UU-CS--,
Institute of Information and Computing Sciences, Utrecht
University, .
[] J. H. Lemelson and R. D. Pedersen, “GPS vehicle collision
avoidance warning and control system and method, Google
Patents, .
[] C. de Fabritiis, R. Ragona, and G. Valenti, Trac estimation
ceedings of the 11th International IEEE Conference on Intelligent
Transportation Systems (ITSC ’08), pp. –, IEEE, Beijing,
China, December .
[] S. F. Smith, G. J. Barlow, X. F. Xie, and Z. B. Rubinstein,
“SURTRAC: scalable urban trac control, in Proceedings of
the Transportation Research Board 92nd Annual Meeting Com-
pendium of Papers (TRB ’13),Washington,DC,USA,.
[] J. J. Blum, A. Eskandarian, and S. A. Arhin, “Intelligent Speed
Adaptation (ISA), in Handbook of Intelligent Vehicles,pp.
, Springer, .
[] K. U. Scholl, “Energy ecient driving, EP Patent no. ,
[] S.Shamshirband,N.B.Anuar,M.L.M.Kiah,andA.Patel,“An
appraisal and design of a multi-agent system based cooperative
wireless intrusion detection computational intelligence tech-
nique, Engineering Applications of Articial Intelligence,vol.,
no. , pp. –, .
[] N. Haworth and M. Symmons, “Driving to reduce fuel
consumption and improve road safety,” Monash University
Accident Research Centre, ,les/arsrpe/
[] T. Lyons, J. Kenworthy, C. Moy, and F. dos Santos, An interna-
tional urban air pollution model for the transportation sector,
Transportation Research D: Transport and Environment,vol.,
[] A. Af W
ahlberg, “Fuel ecient driving training-state of the art
and quantication of eects, in Proceedings of the International
Conference Soric, E, Manama, Bahrain, .
[] S.Fan,Q.Fu,J.Zhang,J.Ma,J.Zhao,andK.Lu,“Researchon
drag reduction of commercial vehicle based on aerodynamics,
in Proceedings of the FISITA 2012 World Automotive Congress,
vol.  of Lecture Notes in Electrical Engineering, pp. –,
Springer, Berlin, Germany, .
[] D. Streimikiene, T. Bale
zentis, and L. Bale
zentiene, “Compara-
tive assessment of road transport technologies, Renewable and
Sustainable Energy Reviews,vol.,pp.,.
[] C. Mazzoleni, H. Moosm
uller, H. D. Kuhns et al., Correlation
between automotive CO, HC, NO, and PM emission factors
from on-road remote sensing: implications for inspection and
maintenance programs, Transportation Research D: Transport
and Environment,vol.,no.,pp.,.
[] X. Yan and R. J. Crookes, Life cycle analysis of energy use
and greenhouse gas emissions for road transportation fuels in
China, Renewable and Sustainable Energy Reviews,vol.,no.
[] B. Masum, H. Masjuki, M. Kalam, I. M. Rizwanul Fattah, S.
Palash, and M. Abedin, “Eect of ethanol-gasoline blend on
emission in SI engine, Renewable and Sustainable Energy
[] H. Yamada, “Contribution of evaporative emissions from gaso-
line vehicles toward total VOC emissions in Japan, Science of
the Total Environment,vol.,pp.,.
[] B. M. Masum, M. A. Kalam, H. H. Masjuki, and S. M. Palash,
“Study on the eect of adiabatic ame temperature on NO
formation using ethanol gasoline blend in SI engine, Advanced
Materials Research,vol.,pp.,.
[] S.M.Palash,M.A.Kalam,H.H.Masjuki,M.I.Arbab,B.M.
Masum, and A. Sanjid, “Impacts of NO
reducing antioxidant
additive on performance and emissions of a multi-cylinder
diesel engine fueled with Jatropha biodiesel blends, Energy
Conversion and Management,vol.,pp.,.
[] I.M.RizwanulFattah,H.H.Masjuki,M.A.Kalam,M.Mojur,
and M. J. Abedin, Eect of antioxidant on the performance
and emission characteristics of a diesel engine fueled with palm
biodiesel blends, Energy Conversion and Management,vol.,
pp. –, .
[] S. Shamshirband, A distributed approach for coordination
between trac lights based on game theory, e International
Arab Journal of Information Technology,vol.,no.,.
[] S. P. Kumar, S. Subbarao, and K. A. Jolapara, “IV and VV
Communication based VANET to optimize fuel consumption
at trac signals, in Proceedings of the 13th International IEEE
Conference on Intelligent Transportation Systems (ITSC ’10),pp.
–, Funchal, Portugal, September .
[] M. Barth and K. Boriboonsomsin, “Trac congestion and
greenhouse gases, Access, no. , pp. , .
[] W. Wen, A dynamic and automatic trac light control expert
system for solving the road congestion problem, ExpertSystems
with Applications,vol.,no.,pp.,.
e Scientic World Journal 
[] N.Maslekar,M.Boussedjra,J.Mouzna,andH.Labiod,“VANET
based adaptive trac signal control, in Proceedings of the IEEE
73rd Vehicular Technology Conference (VTC 11), pp. –, IEEE,
[] C. Li and S. Shimamoto, An open trac light control model for
reducing vehicles'CO
emissions based on ETC vehicles, IEEE
Transactions on Vehicular Technology,vol.,no.,pp.,
novel real-time trac information system based on wireless
mesh networks, in Proceedings of the 10th International IEEE
Conference on Intelligent Transportation Systems (ITSC ’07),pp.
–, IEEE, Seattle, Wash, USA, October .
[] H. Moustafa and Y. Zhang, Vehicular Networks: Techniques,
Standards, and Applications, Auerbach Publications, .
[] M. Oche, R. M. Noor, A. S. Al-jaw, A. T. Bimba, and M. K.
Nasir, An automatic speed violation detection framework for
VA NE Ts , i n Proceedings of the IEEE International Conference
on RFID-Technologies and Applications (RFID-TA ’13),pp.,
Johor Bahru, Malaysia, .
[] T. Yamashita, K. Kurumatani, and H. Nakashima, Approach
to smooth trac ow by a cooperative car navigation system,
Transactions of Information Processing Society of Japan,vol.,
pp. –, .
[] S. Kato, S. Tsugawa, K. Tokuda, T. Matsui, and H. Fujii, “Vehicle
control algorithms for cooperative driving with automated
vehicles and intervehicle communications, IEEE Transactions
on Intelligent Transportation Systems,vol.,no.,pp.,
[] C. Bergenheim, S. Shladover, E. Coelingh, C. Englund, and S.
Tsugawa, Overview of platooning systems, in Proceedings of
the 19th ITS World Congress, Vienna, Austria, October .
[] E. Chan and U. Ricardo, Overview of the SARTRE platooning
project: technology leadership brief, SAE Technical Paper -
-, SAE International, .
[] M. R. Nieuwenhuijze, T. van Keulen, S.
u, B. Bonsen, and
H. Nijmeijer, “Cooperative driving with a heavy-duty truck
in mixed trac: experimental results, IEEE Transactions on
Intelligent Transportation Systems,vol.,no.,pp.,
[] K. Seki and M. Hamaguchi, “Inter-vehicle communication for
truck platooning (nd report): a research in energy ITS project,
in Proceedings of the 17th ITS World Congress,Busan,Republic
of Korea, .
[] M. C. Coelho and N. Rouphail, Assessing the impact of
VV/VI communication systems on trac congestion and
emissions, in Proceedings of the European Conference on
Human Centred Design for Intelligent Transport Systems, Berlin,
Germany, April .
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... From Fig. 1 it has shown that the vehicle runs above the green speed or below the green speed, then it will consume more fuel. The curve c is showing that if the aerodynamic drag is reduced at high speed, then it will consume less fuel [28]. Now to accomplish our research, the proposed algorithm is followed by data collection, data preparation, choose a model, train the model, evaluate the model, parameter tuning, make predictions. ...
... But in congested road conditions, the road is very busy, especially in-office hours. At that time more fuel is consumed [28]. ...
... Free flow condition fuel consumption[28]. ...
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The prediction of vehicle speed plays a significant role in energy management. Predicting vehicle speed can help to designs a wide range of vehicle controllers, especially in the case of an automated self-driving car for efficient fuel management applications. Nowadays the issue of energy saving is becoming more popular. This paper has proposed the prediction of the speed of the vehicle in different road statuses like in road curvature, in traffic, in weather, and indifferent road conditions, etc. Data is collected from different sources. This research is based on a supervised learning algorithm. Under supervised learning, a linear regression algorithm has been used to train up the model. For implementation Python programming language has been used. The proposed algorithm has provided better accuracy and performance than the existing well knew state-of-arts algorithm.
... 3. Communication volume [138]: connected vehicles can receive and provide data through increasingly efficient communication. 4. System robustness [107]: Robust system can react to unexpected events. Two main solutions are available to assess the robustness of CAVs in the event of disruptions: 1) ensuring that all consequences can be predicted in advance or 2) being able to react in time. ...
... As a consequence, intense braking and lane changes at lower speeds may arise. To avoid this congestion, Ioannou et al. [107] propose to inform the CAVs in advance of the lane unavailability. The CAV agent, the incident initiator, or one CV receiving the message, can then disseminate the information. ...
This thesis addresses the problem of communication in the context of a fleet of autonomous and connected vehicles. It is included in the context of intelligent transport systems and the smart city. Numerous applications have been developed in recent years in these areas, each with the objective of improving the quality of life of users. The automation of driving, which has been a central concern of the automotive industry since the 2000s, is expected to reduce the number of accidents, improve the comfort of users and also reduce the ecological footprint of road traffic in general. It also paves the way for the implementation of effective cooperative strategies between vehicles. As cooperation relies on the exchange of relevant information, it is necessary that vehicles are able to know what information to transmit and how. This thesis focuses on inter-vehicle communication and approaches the problem as a complex system in which many vehicles interact with each other, each has its own local objective. Each vehicle perceives information in its local environment and knows that some of this information may be useful to neighbouring vehicles. As a cooperative entity, it will share with its neighbours the information it assumes to be useful. In this work, an inter-vehicle communication is considered useful if it verifies the following two properties: 1) the information exchanged is understood by the receiving vehicle and 2) it brings new knowledge to it. In the context of a fleet of Autonomous and Connected Vehicles (CAVs), these two properties may not always be guaranteed, especially if the vehicles involved do not share the same referential frame (e.g. different units of measurement for the same information) or if the volume of communication exceeds the vehicle's capacity. The contribution of this thesis is twofold: it proposes a first module allowing a vehicle to adapt information to its own reference system, and a second module allowing to optimise information exchanges within a fleet of CAVs. It approaches the problem of a common referential frame between vehicles as a data estimation problem, and that of the optimisation of information exchanges between vehicles as a distributed optimisation problem under constraints. The originality of this work lies in the use of adaptive multi-agent systems (AMAS) to solve them. The AMAS approach is an organisational approach to building complex systems that adapt, continuously and locally, to the dynamics of their environment. It focuses on the interactions between the system and its environment on the one hand and between the parts (agents) of the system on the other. These interactions are based on local and cooperative processing of information by the parts of the system, which only have a partial view of their environment. This principle of locality guarantees the emergent nature of the system's operation. The evaluation of the two modules was carried out using various datasets highlighting disturbances that could affect the system (modification of the environment and intermittence of vehicles in the fleet). The results show that both modules are effective for large-scale problems in a dynamic environment. The use of a local approach to solve the problem avoids an exponential increase in complexity. In the context of optimising the information exchanged, and in order to propose a solution that preserves the confidentiality of the data, the local solution of the problem is not based on the exchange of the CAVs' personal information.[...]
... TMS is an integral part of the Intelligent Transportation system (ITS). There are many applications of ITS in different modes of the transportation system to address problems like accidents rate, traffic congestion, carbon emissions, air pollution [3]. We have found that ITS can also effectively optimize reliability, travel speeds, traffic flow, etc. ...
... From [3], we obtain the equation of Total Fuel Consumption is as follows: ...
There are significant disadvantages of the presently installed traffic management systems. Speed breakers are causing a great deal of discomfort to the passengers and drivers and damage the vehicles. It can increase pollution as vehicles in a lower gear consuming more fuel per mile. Moreover, the transportation sector contributes nearly 29% of Green House Gases (GHG) emissions. But still, there is no infrastructure to measure pollutant emission of the vehicle in the important juncture of roads like toll plaza. This chapter proposes an idea of an Internet of Things (IoT) enabled system which ensures the increase of barricades according to the incoming vehicle's speed and monitors lane congestion. It would remove the barricades (depends on location) at night time when there is no such need for speed reduction. This system would help reduce the stop-and-go time of the vehicle and select journey time when there is less traffic on the road, thereby increasing the vehicle's fuel efficiency. Additionally, real-time emissions of the vehicles can be captured by the system for monitoring pollution data. This large data can be mined in the Big Data platform. This pollution data would be time-stamped with the Electronic Toll Collection System- FASTag database to detect the polluting vehicles and notifying those using IoT techniques. Objective of this chapter is to develop a low-cost IoT system that can be efficiently used for traffic management to improve lane congestion problems, reduce fuel consumption of vehicles and building a GHG inventory of vehicle effluents. It can reduce annually 106257.54 kg of CO2 (Annual Carbon footprint) for a particular toll plaza area.
... However, it decreases the fuel economy rate [22,35]. On the other hand, the fuel consumption and the fuel economy rates depend not only on driving speed but also on the accelerating, decelerating, or idling of vehicles [36,37]. The fuel consumption and fuel economy rates for LDVs are shown in Figure 3 and Figure 4, respectively. ...
Full-text available
Correct emission factors are necessary for evaluating vehicle emissions and making proper decisions to manage air pollution in the transportation sector. In this study, using a chassis dynamometer at the Automotive Emission Laboratory, CO2 and CH4 emission factors of light-duty vehicles (LDVs) were developed by fuel types and driving speeds. The Bangkok driving cycle was used for the vehicle’s running and controlling under the standard procedure. Results present that the highest average CO2 and CH4 emission factors were emitted from LDG vehicles, at 232.25 g/km and 9.50 mg/km, respectively. The average CO2 emission factor of the LDD vehicles was higher than that of the LDG vehicles, at 182.53 g/km and 171.01 g/km, respectively. Nevertheless, the average CH4 emission factors of the LDD vehicles were lower than those of the LDG vehicles, at 2.21 mg/km and 3.02 mg/km, respectively. The result reveals that the lower driving speed emitted higher CO2 emission factors for LDVs. It reflects the higher fuel consumption rate (L/100 km) and the lower fuel economy rate (km/L). Moreover, the portion of CO2 emissions emitted from LDVs was 99.96% of total GHG emissions. The CO2 and CH4 emission factors developed through this study will be used to support the greenhouse gas reduction policies, especially concerning the CO2 and CH4 emitted from vehicles. Furthermore, it can be used as a database that encourages Thailand’s green transportation management system.
... A lowspeed (< 40 km/h) congested traffic has severe consequences on vehicular emissions ranging to about 20 to 50% higher values than at speed range of 75 km/h. Similarly, Huboyo et al. (2017) and Nasir et al. (2014) decrypted that speed range of 45 to 80 km/h is the most efficient operating conditions for functioning of conventional vehicles. Consequently, the observations in this study comprehend that the traffic speed and vehicular operations at both urban and suburban region is far beyond the optimum speed levels, resulting in curtailment of efficient performance of the vehicles. ...
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The present study discusses on real-world operating scenario of widely accepted electric vehicles — electric two-wheelers and three-wheelers. Use of electric three-wheelers remains majorly restricted to developing regions, whereas electric two-wheelers have a widespread stakeholder base. However, the majority of these vehicles demonstrate maximum speed of 25 km/h, constrained by design specifications. Study revealed that electric two-wheelers and three-wheelers exhibited specific energy consumption of 28.67 Wh/pkm and 43.25 Wh/pkm, respectively, based on a case study for the state of West Bengal, India. Predominant charging regime of target vehicles from domestic source, powered by nationalized grid, leads to high use-phase emission, on a plant-to-wheel approach. Results on traffic dynamic behavior revealed that target electric variants pose to be potential candidates augmenting congestion effect on already running conventional traffic. Hence, two scenarios need to be addressed: (a) regulating the operation of low-speed electric vehicles and (b) optimizing the parameters governing use-phase emissions. Graphical abstract