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Research Article
Reduction of Fuel Consumption and Exhaust Pollutant
Using Intelligent Transport Systems
Mostofa Kamal Nasir,
1
Rafidah Md Noor,
1
M. A. Kalam,
2
and B. M. Masum
2
1
Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
2
Centre for Energy Sciences, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
Correspondence should be addressed to M. A. Kalam; kalam@um.edu.my
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
cited.
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 trac ow smooth and to reduce trac congestion and emission of greenhouse
gas. Automobile emits a massive amount of pollutants such as Carbon Monoxide (CO), hydrocarbons (HC), carbon dioxide (CO
2
),
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. 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 suers heavily from environmental pollution
[, ]. Hence the reduction of fuel consumption can mini-
mize the pollutant emission and preserve the environment
clean and green []. ough signicant 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 eciency
and economically viable environment friendly technology
[, ].
ITS can be dened as wire and wireless communications
basedoninformationandelectronicstechnologiesintegrated
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 trac
information systems (TIS), electronic toll collection system
(ETCS), and automated trac 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
http://dx.doi.org/10.1155/2014/836375
e Scientic World Journal
isapromisingtechnologythatcanbeusedforreducingfuel
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 trac density estimation [].Itcanbeusedforgreen
purposes by informing the driver of the best path that can
reduce the signicant amount of fuel as the vehicle choice is
the less congested route [].
Vehicles can send and receive message with important
dataandsendforbestpathaccordingtotheirlocation,speed,
and direction []. An intelligent vehicle collects data using
some special sensors. Aer processing this data, it broadcasts
the information to other vehicles. Majority of vehicles in
present days run on fossil fuels [, ]. Hence, signicant
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 ecient path [].
is paper survey is to nd out the eect of ITS tech-
niques and technologies on energy saving and reduction of
environmental pollution from vehicles and road transporta-
tion systems including VV and VI, 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-
state-of-artgreensolution,andnallyacasestudyadvocates
the issues.
2. Literature Review
2.1. ITS Technology. ere are a number of techniques and
technologiesusedforthereductionoffuelconsumptionto
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
theaveragedistancecanbedonethroughtracreductionby
navigation and trac 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 trac congestion [].
e ITS techniques and technologies can reduce energy
consumption by changing the driving behavior, suggesting
congestion free smooth path, automatic trac 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
2
4
6
8
10
12
14
16
18
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
Speed (km/h)
AB
C
Electric and
hybrid car
Green speed
Fuel consumption (L/100 km)
F : Relation between fuel consumption and average speed.
0
2
4
6
8
10
12
14
16
18
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 eciency
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
consumptionforthehybridandelectricvehicleisshownby
doted das line.
Figure shows how the fuel consumption varies accord-
ingtogearchangeofamanualdrivingcar.ebestwayto
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
gear.elowerspeedratiosarethemostfuelguzzlingbecause
theyareassociatedwithanenginethatisnotsuciently
loaded. e manual transmission vehicle goes to the highest
speed ratio as soon as possible. When going up a slope, avoid
shiing to a lower gear as much as possible to keep engine
loaded.Asthisapproachesastop,shitoalowergearwithout
braking so as to recover energy over a greater distance. With
an automatic transmission, it is more dicult 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
ratio.
e Scientic World Journal
T : Techniques and Technologies for fuel reduction of vehicle.
Reduction
Parameter
Reduction Type Attribute
Techniques
Technologies
Fuel Reduction
Importance of
Reduction of Fuel
Consumption for
Green Driving
Vehicles
Improvement of Fuel
Eciency of Vehicle By
Upgrading Mechanical
Properties
Upgrading
Mechanical
Properties
Roadways
Improvement of Highways
Upgrading Civil
Properties
Reduction of Fuel
by Intelligent
Driving
Green Driving
Behavior
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
Intelligent
Manage-
ment of
Highways
Lane
Electronic Toll Collection
Trac Trac Light Control
Collision Avoidance
Maximize
roughput
Intelligent
Navigation System
Bottleneck Elimination
Electronic Toll
Collection
Shortest
Distance
Trac Reduction
by Navigation
Increase
Transportation
Eciency
Occupancy Increase
Car Sharing, Car
Pool,
Other Eective Factor
For Transportation
Multi-Modality
Public
Transportation
Trac Reduction
by Transportation
Reduction
Minimization Of
Transportation
Demand Management
Road Pricing
Parking Strategies
No Transportation
Communication
VA NE T
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 dierences 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
𝑥
versus average speed. Masum et
al. []reportedthatNO
𝑥
increases with engine speed as
more fuel is burnt resulting in high in-cylinder temperature
at high speeds. NO
𝑥
emission increases more than linearly
with the increase of average speed [, ]. At lower speed
NO
𝑥
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 eciency
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 Trac Signal Control. e ITSC system plays
an essential role in both safety and eciency of road trac
[]. e target of the ITSC system is the reduction of
congestion queue time in trac signal. ITSC reduces the
waiting time in trac control signal []. ITSC uses a wireless
communication between RSU and vehicle []. e eects of
ITSC are the reduction in congestion, the economic eect,
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 Scientic World Journal
0
10
20
30
40
50
60
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140
Emission rate (g/km)
Average speed (km/h)
CO
(a)
Emission rate (g/km)
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140
Average speed (km/h)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
VOC
NO
x
(b)
F : Typical relation between emission and average speed. (a) CO versus average speed and (b) NO
𝑥
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 innity []. Vehicles need to
be smoothed for reducing CO
2
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 trac light control model []
is shown in Figure .
(i) Tier-: tier- is responsible for collecting trac infor-
mation, receiving light phase data, and sending trac
ow data and it also calculates the suggested speeds.
GPS devices will provide the vehicle state informa-
tion. To transmit the current trac information to
ITSC, vehicle uses the OBU devices. e OBU will
calculate the recommended speed when vehicles get
the trac information from the trac 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
trac ow data and sends the control result to the
ITSC from the OBUs. It has three parts, that is,
antennas, storage, and trac lights. e ETC’s OBU
devices antennas in tier- can communicate with
other devices by wireless communications; hence,
the trac light will receive the real-time trac ow
information. At the same time, the trac control
results will be sent to ECT’s OBU and then drivers can
know the trac light phases in time. e purpose of
thestorageistosavethereceivedtracowsdata.
etraclightsarethedisplaysthatshowthecontrol
results.
(iii) Tier-: data processing task is done in tier- from
the three sections. Data extraction is in Section .e
antenna periodically accepts the trac information
from the vehicles. Data processing task is done in
this tier and data is fed from the tier- of ITSC.
Road trac 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 trac
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
eciency 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.
ByusingtheETCS,thefactorofCO,HC,andNO
𝑥
levels
is reduced at a signicant level. is analysis also showed that
the air pollution emission levels at the toll booth links are
reduced for all pollutants.
2.2.3. Trac 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 trac
congestion. e trac information system gathers the trac
data and transmits this data to the driver in the roads [].
In VANET, every vehicle periodically exchanges information
every ms. e trac density is the most inuential
factor that aects the average speed of the vehicle [, ].
ITS application performance depends on how accurately it
can measure the trac ow rate, trac density, and mean
speed of the vehicle. VANET is a high mobility network
e Scientic World Journal
ird party
application
Trac control center
Data
extraction
Trac light control
CO
2
emission calculation
Trac light/storage/antenna
Data collection
storage
Result
display
GPS and ETC OBU
Trac flow collection
Recommended speed calculation
Tier-3
Tier-2
Tier-1
ETC
GPS
F : ree-tier open trac control system.
Monitoring
camera
Antenna
Antenna
Vehicle
detector
Vehicle
detector
Vehicle gate
On-board
equipment
Vehicle height
detector
Display
Display
Intercom
Vehicle detector
F : Electronic toll collection system.
that greatly aects the green measures. Fuel consumption
varies due to dierent speeds, accelerations, stop-and-go
times, dierent followed routes, and the level of trac
congestion.
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 VV 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 VV
communication is the compatibility of the real-time data
transmission required for automated driving.
2.2.5. Platooning. e platooning can be dened as a collec-
tion of vehicles that travel together and actively coordinate
information []. Platooning oers a list of advantages
including increase of fuel and trac eciency, safety, and
drivingcomfort.emaingoaloftheplatoonistoberelieved
from the trac 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 trac than current manual system.
is obviously shrinks the trac 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 eciency and emission reduction by to %. For
e Scientic 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
Communication
Systems (VICS)
Provide the trac and travel data to the drivers by
transmitting using wireless technology.
Reducing trac congestion,
trac accidents, and improving
road environment
Glass et al. []
Trac 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
greener.
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 ecient path.
Pfeier et al. []
Driver Assistance
Systems
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
System
Real-time driving functions necessary to drive a
ground-based vehicle without real-time input
from a human operator.
Trac-jam reduction and
full-range automated cruise
control
Masum et al. []
Urban Trac
Information Systems
(UTIS)
Create, analyze and process the location
information of moving vehicle to improve
convenience by providing improved ow of
transportation logistics and analyzed trac
information to driver.
Total management system of the
streetlight light and security light
and reduction of pollution
Wiering et al. []
Intelligent Trac Light
Control System.
Intelligent trac 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 trac lights.
Maximize the trac eciency of
intersection of roads and
achieving a best control for
trac.
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
congestion.
de Fabritiiset al. []
Trac Estimation and
Prediction System
Use computer, communication, and control
technologies to monitor, manage, and control the
transportation system.
Improve trac conditions and
reduce travel delays.
Smith, et al. []
Scalable Urban Trac
Control
e SURTRAC dynamically optimizes the control
of trac signals in three sections: rst, decision
making in decentralized manner of individual
intersections; second is an emphasis on real-time
responsiveness to changing trac condition and
nally managing urban road networks.
Objectives include less waiting,
reduced trac 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
Reckoning
ISA helps to reduction of
accident risks and reductions of
noise and exhaust emissions.
thesereasonsanumberofplatooningprojectshavebeen
continuing such as SARTRE [], a European platooning
project; PATH [], a California trac 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 trac information and the green navigation
system will update the trac information to modify
the planned path adaptively.
(ii) Calculate accurately the vehicle ow rate based on the
trac ow theory.
(iii) To estimate the vehicle density on specic time use
historical trac information.
(iv) Try to maintain the average green speed (–
km/h) to get fuel eciency as well as pollutant at
minimum level.
e Scientic 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 trac better.
3.1. Model Assumption. To achieve the objective behind
developing a fuel ecient route selection model, some
assumptions need to be agreed on to fulll 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 trac 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 trac.
An ecient fuel saving navigation system estimates the green
optimum path []. A green navigation system provides
suggestion for fuel ecient route to driver based on available
information about fuel dependent parameter of each vehicle
for unraveling trac 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 ecient paths to destination considering current
and historical trac data. In ITS technology, a number of
sensors are installed in the road section to nd out the vehicle
density, trac 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, trac ow rate, and
the vehicle mean speed.
3.2. Vehicle Density. Vehicle density referred to the number
of vehicles per kilometer in a specic 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
1
,
1
,
1
=
,
()
where
1
isthemeasuredlocationand
1
is the time interval
and
1
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
1
,
1
,
1
=
⋅
⋅
.
()
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
Area
(
)
.
()
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
2
and a time interval
of measurement interval
2
can be dened as follows.
For a time interval at any location
2
, the ow rate is
Φ
2
,
2
,
2
=
.
()
e number is the total number of vehicles that pass
through the location
2
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
Φ
2
,
2
,
2
=
⋅
⋅
=
Total Distance Covered by Vehicles in
2
Area
2
.
()
From () we can nd the general denition for vehicle ow
rate as follows:
Φ
(
,,
)
=
⋅
⋅
=
Total Distance Covered by Vehicles in
Area
(
)
;
()
is the total distance covered by the vehicle.
e vehicle ow rate versus hour report provides a
graph report that shows the historical trac 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 trac and it is useful to make a decision for green
route selection. Figure showsatypicaltracowversus
time of day.
3.4. Vehicle Mean Speed. e vehicle mean speed can be
dened 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 Scientic World Journal
0
10000
20000
30000
40000
50000
60000
0 2 4 6 8 1012141618202224
Vehicles (vph)
Time of day
F : Typical trac ow versus time of day.
a relationship with vehicle density and vehicle ow rate as
follows:
(
,,
)
=
(
,,
)
(
,,
)
=
Total distance covered by vehicles in
Total time spent by vehicles in
.
()
From ()wecanrewritethevehiclemeanspeedasthe
fundamental relation of trac ow theory as follows:
Φ=⋅.
()
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
(
,,
)
=
1
𝑛
V
𝑖
.
()
From ()and() we can easily nd the mean speed
(
,,
)
=
1
(
1/
)
∑
𝑚
1/V
𝑓
.
()
4. Methodology
e proposed green fuel ecient route choice procedure
uses dierent ITS technologies. e green navigation method
nds the multiple candidates for a specic journey and
chooses the most fuel ecient route. e method avoids
manual trac signal and toll collection and does not select
a route to a destination in which a trac jam might happen.
e most fuel ecient route between sources to destination
may be dierent from the shortest and fastest routes. ere
are several factors that aect the fuel consumption on streets.
ese parameters are classied into four categories, that
is, static street parameters, dynamic street parameters, car
specic 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
andthenumberoftraclightsonthestreetremainsmore
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
eciency 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 person’s 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 trac streets. We
will summarize this model below. e input to the model
includes
(i) static street parameters: number of stop signs (ST)
from source to destination;
(ii) dynamic street parameters: V, V
2
,andV
3
,whereV is the
vehicle means speed on a specic 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 =
𝑛
𝑖
𝑖
V
𝑖
+
𝑐
𝑚
𝑗
𝑗
,
()
where TFC = Total fuel consumption,
𝑖
= length of road
section i (
𝑖+1
−
𝑖
), V
𝑖
= mean speed of road section
𝑖
,
𝑐
=
fuel consumption per second while vehicle at idle, and
𝑖
=
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 ecient 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 ecient routes potentially dierent 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 dierent scenarios,
thatis,freeowcondition,moderatecongestion,andheavy
congestion.
Figure shows three dierent 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 Scientic World Journal
F : ree dierent routes of the same origin and destination.
0
2
4
6
8
10
12
14
16
Tot a l time
(min)
Fuel used
(L)
Route 1
Route 2
Route 3
Distance
(km)
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 oce time from :
am to : am and second one is : pm to : pm.
5. Result and Discussions
5.1. Free Flow Condition. Byillustratingthefreeowcondi-
tion, the shortest distance route is also fuel ecient and
also emits relatively lower pollutant. Table shows all the
data found in free ow condition in three dierent routes.
Figure shows the bar graph for the distance, total travel
times,andfuelusedinfreeowconditioninthreedierent
routes.
5.2. Moderate Congestion. To demonstrate the moderate
congestion condition, Table shows the detailed data of this
casestudy.Normallyatthenoontimethecongestionof
theroadistolerableandthetracdensityoftheroadis
at random manner. is time route performs the most fuel
0
5
10
15
20
25
Tot a l time
(min)
Fuel used
(L)
Route 1
Route 2
Distance
(km)
Route 3
F : Bar graph for the distance, total travel times, and fuel used
in moderate congestion.
0
5
10
15
20
25
30
35
Distance
(km)
Tot a l time
(min)
Fuel used
(L)
Route 1
Route 2
Route 3
F : Bar graph for the distance, total travel times, and fuel
used in heavy congestion.
ecient and environment friendly; it may dier from other
times.
Figure shows the bar graph for the distance, total travel
times, and fuel used in average congestion in three dierent
routes.
5.3. Heavy Congestion. In a heavy congested condition the
road is very rushy as at morning most of the travelers go for
workandataernoontheygobackhomefromwork.Table
shows the details of the study; route is more fuel ecient
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 dierent
routes.
e Scientic 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 eciency. e important issues
of green technologies are related to energy eciency in auto-
mobile industry and promote environment friendly commu-
nication technologies and systems. Green ITS technologies
play a signicant role in reducing energy consumption
inautomobileandroadtransportsystemforavarietyof
applications. is paper provides a survey on the eects of
ITS related techniques on the reduction of fuel consumption
and exhaust pollutant. In ITS, most of the applications are
for highlighting trac 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
trac ow data as well as historical trac information. A
case study shows that if the driver uses the green navigation
system,itwillsavefuelandreducetheenvironmentpollution.
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 signicant contribution in terms of energy and
environment.
Nomenclature
AETAT: Association of electronic technology for
automobile trac
ADS: Automated driving system
ATLCS: Automated trac light control system
CMEM: Comprehensive modal emissions model
CO: Carbon monoxide
CO
2
: Carbon dioxide
DAS: Driver assistance systems
DSRC: Dedicated short range communication
EPA: Environmental protection agency
ETCS: Electronic toll collection system
ETC: Electronic trac control
FTP: Federal test procedure
GHG: Greenhouse gas
GNS: Green navigation system
GPS: Global position system
e Scientic World Journal
HC: Hydrocarbons
ISA: Intelligent speed adaptation
IT: Information technology
ITLCS: Intelligent trac 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
NO
𝑥
: Oxides of nitrogen
NS: Navigation system
OBU: On-board unit
ORNL: Oak Ridge National laboratory
RSU: Road side unit
SURTRAC: Scalable urban trac control
TEPS: Trac estimation and prediction system
TIS: Trac information systems
TMS: Trac management systems
UTIS: Urban trac information systems
VI: Vehicle-to-infrastructure (VI)
VV: 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
systems
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 conict of interests
regarding the publication of this paper.
Acknowledgments
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
support.
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