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
Nowadays the energy saving issue is becoming more popular in ITS. Recent increases in fuel prices have a great impact on global economic changes. The 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 [
ITS can be defined as wire and wireless communications based on information and electronics technologies integrated with transportation system and vehicles [
Vehicles can send and receive message with important data and send for best path according to their location, speed, and direction [
This paper survey is to find out the effect of ITS techniques and technologies on energy saving and reduction of environmental pollution from vehicles and road transportation systems including V2V and V2I, a green navigation system which helps to find out the best path for the minimization of fuel consumption and exhaust pollutant to provide the-state-of-art green solution, and finally a case study advocates the issues.
There are a number of techniques and technologies used for the reduction of fuel consumption to make the environment greener. ITS could be used for reduction of fuel consumption which would make the environment clean and green [
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 Efficiency 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 The Ride | |||||
Get Rid of Weight and Reduce the Drag | |||||
Avoid Unnecessary Idling | |||||
Use Latest Technology Car | |||||
Traffic Flow | Intelligent Management of Highways | Lane | |||
Electronic Toll Collection | |||||
Traffic | Traffic Light Control | ||||
Collision Avoidance | |||||
Maximize Throughput | Intelligent Navigation System | ||||
Bottleneck Elimination | Electronic Toll Collection | ||||
|
|||||
Shortest Distance | Traffic Reduction by Navigation | Increase Transportation Efficiency | Occupancy Increase | Car Sharing, Car Pool, | |
Other Effective Factor For Transportation | Multi-Modality | Public Transportation | |||
Traffic Reduction by Transportation Reduction | Minimization Of Transportation | Demand Management | Road Pricing | ||
Parking Strategies | |||||
No Transportation | Communication | VANET | |||
City Planning | Compact City |
The ITS techniques and technologies can reduce energy consumption by changing the driving behavior, suggesting congestion free smooth path, automatic traffic control signal, electronic toll collection, and platooning. From the mechanical properties of the vehicle the automobile engineer proved that the vehicle running 50–70 km/h for gasoline engines and 50–80 km/h for the petrol engine consumed the lowest rate of fuel. Figure
Relation between fuel consumption and average speed.
Figure
Relation between fuel consumption and gear change of a manual driving car.
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 differences avoid using the speed regulator to maintain a constant speed, as the gearbox will shift to a lower speed and will increase the engine speed when going up a slope in order to maintain the same speed [
Typical relation between emission and average speed. (a) CO versus average speed and (b)
A number of ITS applications have to reduce the fuel consumption and exhaust pollutant. The ITS related technologies are described below.
The ITSC system plays an essential role in both safety and efficiency of road traffic [
Three-tier open traffic control system.
Tier-1: tier-1 is responsible for collecting traffic information, receiving light phase data, and sending traffic flow data and it also calculates the suggested speeds. GPS devices will provide the vehicle state information. To transmit the current traffic information to ITSC, vehicle uses the OBU devices. The OBU will calculate the recommended speed when vehicles get the traffic information from the traffic lights. By using the ITSC the drivers may minimize the waiting time and also minimize number of stops.
Tier-2: tier-2 controls the receiving and saving of traffic flow data and sends the control result to the ITSC from the OBUs. It has three parts, that is, antennas, storage, and traffic lights. The ETC’s OBU devices antennas in tier-1 can communicate with other devices by wireless communications; hence, the traffic light will receive the real-time traffic flow information. At the same time, the traffic control results will be sent to ECT’s OBU and then drivers can know the traffic light phases in time. The purpose of the storage is to save the received traffic flows data. The traffic lights are the displays that show the control results.
Tier-3: data processing task is done in tier-3 from the three sections. Data extraction is in Section
ETCS is a system that permits for collection of toll payments and traffic monitoring electronically by uninterruptedly of vehicle moving [
Electronic toll collection system.
By using the ETCS, the factor of CO, HC, and
TIS is very important for ITS application. The information about the number of vehicles in the road is very important to eliminate the traffic congestion. The traffic information system gathers the traffic data and transmits this data to the driver in the roads [
The cooperative driving is an automatic driving of over 2 or 3 lanes used for openly lane changing, merging, and splitting for congestion free driving. The main aim of the cooperative driving is to save the energy and to minimize the air pollution [
The platooning can be defined as a collection of vehicles that travel together and actively coordinate information [
The summary of the ITS applications is given in Table
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 Heft [ |
Vehicle Information Communication Systems (VICS) | Provide the traffic and travel data to the drivers by transmitting using wireless technology. | Reducing traffic congestion, traffic accidents, and improving road environment |
Glass et al. [ |
Traffic 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 efficient path. |
Pfeiffer 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. | Traffic-jam reduction and full-range automated cruise control |
Masum et al. [ |
Urban Traffic Information Systems (UTIS) | Create, analyze and process the location information of moving vehicle to improve convenience by providing improved flow of transportation logistics and analyzed traffic information to driver. | Total management system of the streetlight light and security light and reduction of pollution |
Wiering et al. [ |
Intelligent Traffic Light Control System. | Intelligent traffic 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 traffic lights. | Maximize the traffic efficiency of intersection of roads and achieving a best control for traffic. |
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. [ |
Traffic Estimation and Prediction System | Use computer, communication, and control technologies to monitor, manage, and control the transportation system. | Improve traffic conditions and reduce travel delays. |
Smith, et al. [ |
Scalable Urban Traffic Control | The SURTRAC dynamically optimizes the control of traffic signals in three sections: first, decision making in decentralized manner of individual intersections; second is an emphasis on real-time responsiveness to changing traffic condition and finally managing urban road networks. | Objectives include less waiting, reduced traffic congestion, shorter trips, and less pollution. |
Blum et al. [ |
Intelligent Speed Adaptation (ISA) | There 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. |
Design of dynamic green driving advisor should satisfy the following goals and requirements. Use ITS techniques and technologies to gather the real-time traffic information and the green navigation system will update the traffic information to modify the planned path adaptively. Calculate accurately the vehicle flow rate based on the traffic flow theory. To estimate the vehicle density on specific time use historical traffic information. Try to maintain the average green speed (50–80 km/h) to get fuel efficiency as well as pollutant at minimum level. Design of dynamic speed limit should satisfy the goals and requirements of green driving. The strategy should work even when only one vehicle is doing green driving; more vehicles doing green driving would smooth traffic better.
To achieve the objective behind developing a fuel efficient route selection model, some assumptions need to be agreed on to fulfill 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. These include the OBU, preloaded digital road maps, GPS, and NS. Each vehicle equipped with OBU system collects its own traffic information, including location, spacing, velocity, and acceleration, from GPS device [
Vehicle density referred to the number of vehicles per kilometer in a specific time. Vehicle density
Vehicle flow rate is the number of vehicles that pass through a certain road section per time unit. The vehicle flow rate
For a time interval
The vehicle flow rate versus hour report provides a graph report that shows the historical traffic flow volumes and average speed of the transportation network during a selected time period of the day. This information is useful for analyzing the historical performance of the transportation network and implementing proactive measures to improve the flow of traffic and it is useful to make a decision for green route selection. Figure
Typical traffic flow versus time of day.
The vehicle mean speed
The proposed green fuel efficient route choice procedure uses different ITS technologies. The green navigation method finds the multiple candidates for a specific journey and chooses the most fuel efficient route. The method avoids manual traffic signal and toll collection and does not select a route to a destination in which a traffic jam might happen. The most fuel efficient route between sources to destination may be different from the shortest and fastest routes. There are several factors that affect the fuel consumption on streets. These parameters are classified into four categories, that is, static street parameters, dynamic street parameters, car specific 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 and the number of traffic lights on the street remains more or less constant. The dynamic street parameters are characteristics that change with time. for example, the congestion levels on a street or the average speed on a street. The static and dynamic street parameters together determine the fuel efficiency 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. These parameters account for the variation in fuel consumption due to the car type and the driver behavior. The proposed system is a linear model that can accurately predict the fuel consumption across urban traffic streets. We will summarize this model below. The input to the model includes static street parameters: number of stop signs (ST) from source to destination; dynamic street parameters:
The 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
The final model is expressed as
As stated before, the shortest path route or minimum travel time route may not always be the fuel efficient 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 efficient routes potentially different 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 different scenarios, that is, free flow condition, moderate congestion, and heavy congestion.
Figure
Three different routes of the same origin and destination.
By illustrating the free flow condition, the shortest distance route 2 is also fuel efficient and also emits relatively lower pollutant. Table
Free flow Condition Fuel Consumption.
Performance Measure | Route 1 | Route 2 | Route 3 | Remarks |
---|---|---|---|---|
Distance (Km) | 12.1 | 10.8 | 11.2 | |
Running time (Minutes) | 12 m | 11 m | 12 m | |
Stop time (Minutes) | 2 m | 2 m | 2 m | |
Total time (Minutes) | 14 m | 13 m | 14 m | |
Total distance w.r.t. time | 14 Km | 13 Km | 14 Km | Assumption-1 |
Fuel used (Liter) | 1.82 | 1.69 | 1.456 | |
Fuel consumption (Lt/Km) | 0.13 | 0.13 | 0.13 |
Bar graph for the distance, total travel times, and fuel used in free flow condition.
To demonstrate the moderate congestion condition, Table
Performance on moderate congestion road condition.
Performance Measure | Route 1 | Route 2 | Route 3 | Remarks |
---|---|---|---|---|
Distance (Km) | 12.1 | 10.8 | 11.2 | |
Running time (Minutes) | 17 m | 18 m | 18 m | |
Stop time (Minutes) | 4 m | 4.5 m | 4 m | |
Total time (Minutes) | 21 m | 22.5 m | 22 m | |
Total distance w.r.t. time | 21 Km | 22.5 Km | 22 Km | Assumption-1 |
Fuel used (Liter) | 2.73 | 2.925 | 2.86 | |
Fuel consumption (Lt/Km) | 0.13 | 0.13 | 0.13 |
Figure
Bar graph for the distance, total travel times, and fuel used in moderate congestion.
In a heavy congested condition the road is very rushy as at morning most of the travelers go for work and at after noon they go back home from work. Table
Performance on heavy congested road condition.
Performance Measure | Route 1 | Route 2 | Route 3 | Remarks |
---|---|---|---|---|
Distance (Km) | 12.1 | 10.8 | 11.2 | |
Running Time (Minutes) | 20 m | 21 m | 18 m | |
Stop Time (Minutes) | 8 m | 9 m | 8 m | |
Total time (Minutes) | 28 m | 30 m | 26 m | |
Total distance w.r.t. time | 28 Km | 30 Km | 26 Km | Assumption-1 |
Fuel used (Liter) | 3.64 | 3.9 | 3.38 | |
Fuel Consumption (Lt/Km) | 0.13 | 0.13 | 0.13 |
Bar graph for the distance, total travel times, and fuel used in heavy congestion.
Green technology is one of the most important considerations on developing ITS, foster environmental sustainability, and the economics of energy efficiency. The important issues of green technologies are related to energy efficiency in automobile industry and promote environment friendly communication technologies and systems. Green ITS technologies play a significant role in reducing energy consumption in automobile and road transport system for a variety of applications. This paper provides a survey on the effects of ITS related techniques on the reduction of fuel consumption and exhaust pollutant. In ITS, most of the applications are for highlighting traffic 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 traffic flow data as well as historical traffic information. A case study shows that if the driver uses the green navigation system, it will save fuel and reduce the environment pollution. 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 significant contribution in terms of energy and environment.
Association of electronic technology for automobile traffic
Automated driving system
Automated traffic light control system
Comprehensive modal emissions model
Carbon monoxide
Carbon dioxide
Driver assistance systems
Dedicated short range communication
Environmental protection agency
Electronic toll collection system
Electronic traffic control
Federal test procedure
Greenhouse gas
Green navigation system
Global position system
Hydrocarbons
Intelligent speed adaptation
Information technology
Intelligent traffic light control system
Intelligent transport system
Intervehicle communication
Light duty truck
Least square regression
Modal emission cycle
Measures of effectiveness
Oxides of nitrogen
Navigation system
On-board unit
Oak Ridge National laboratory
Road side unit
Scalable urban traffic control
Traffic estimation and prediction system
Traffic information systems
Traffic management systems
Urban traffic information systems
Vehicle-to-infrastructure (V2I)
Vehicle-to-vehicle
Vehicular ad hoc network
Vehicle collision avoidance system
Vehicle information communication systems
Vehicle miles travelled
Vehicle navigation system
Volatile organic compounds
Wireless access for vehicular environment.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The 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/09 for their support.