29-04-2013, 04:55 PM
ROAD TRAFFIC DECISION SUPPORT SYSTEM
[attachment=53269]
ABSTRACT:
Road traffic management has long been a complex issue and seems likely to continue to be so. Road traffic laws and policies depend on a large number of factors. Making a correct decision for traffic management can be difficult because decision-makers need to analyze and absorb a large quantity of information. This information can be vague and sometime conflicting in nature. Therefore, there is a need for a
better control and a reliable and consistent system to help simplify the traffic decision making process. By incorporating the variables involved in traffic management such as the numbers of accidents, traffic violations, and traffic policemen on duty into an artificial intelligence technique, it is possible to build a traffic decision making system to help decision makers for analysis of traffic laws and policies. This paper proposes a decision support system using fuzzy logic to provide recommendations on whether traffic decision makers should, for example, give priority to increasing the number of traffic policemen on duty, increasing fines, or installing better/more safety devices. Fuzzy Logic allows us to measure imprecise and dynamic factors and to arrive at a reasonable judgment. A case study for Riyadh Region Traffic in Saudi Arabia has been analyzed using the proposed decision making system.
Index Terms:-Traffic Management, Decision support systems, Fuzzy logic.
INTRODUCTION:
Very year, lots of people die or suffer accidents because of factors that could have easily been prevented. The growing number of vehicles, ignorance of safety norms and reckless driving have attributed to the huge number of
Accidents. For example, in Saudi Arabia, there were 5168 fatalities in 2005 alone. This translates to 14 people died on roads in any given day. [1] The World Health Organization described traffic accidents as the "Epidemic of Civilized Societies". It is sad to note that these people just wanted to go home, visit a friend, or go to their work but encountered something terrible along the way. Although governments have existing traffic policies and continually strive to improve road safety, accidents always seem to happen.
THE PROBOSED DECISION SUPPORT MODEL:
The first and probably most important task in the process of designing a fuzzy logic decision support system is the identification of influencing parameters and to their relation on decision making process. In a traffic decision-making system, the traffic factors contributing primarily to a decision making and the typical traffic decisions are the fuzzy variables. After a discussion with number of personnel who had expert knowledge of road traffic in Riyadh Region Traffic decision-making, five influencing factors and four typical decisions were determined. All these factors and decisions are highlighted below in some detail.
Influencing Factors:
The first and most important factor is the number of traffic violations, which shows how many times traffic laws, has been broken. The number of traffic violations factor is considered as one available assessment criterion simply because of the fact that a large number of traffic violations would suggest that the traffic system was out of control, either because of a shortage of available personnel and equipment such as traffic policemen or traffic cameras, or because that the punishments and fines for having broken the law is not acting as a deterrent to drivers. The number of traffic accidents is a second important factor influencing the making of most traffic decisions making. It is a fact that reducing the number of traffic accidents is the principal goal of all traffic managements.
Typical Decisions:
There are a number of typical decisions made by traffic policy maker based on the identified influencing parameters. Launching a traffic awareness campaign is the first typical
decision. The wide concept underpinning traffic awareness campaigns is creating awareness about motorist and pedestrian safety, in order to minimize or prevent traffic accidents.
CONCLUSION:
Making the correct decision at the right time has been a major problem in road traffic managements, because decision-makers need to analyze and absorb a large quantity of information in a short time. The information can be vague and sometime conflicting in nature. This paper presented a fuzzy based decision support system to assist road traffic management. All parameters of the problem that was required to build the system have been defined and modeled using fuzzy sets. The traffic rules for the fuzzy inference system were extracted from traffic experts and historical data. In order to test and evaluate the proposed system, it has been demonstrated for a case study of Riyadh Region Traffic. The proposed system has shown that Fuzzy logic based approach has a considerable potential to be used in the development
of a road traffic management system to support decision-takers.
[attachment=53269]
ABSTRACT:
Road traffic management has long been a complex issue and seems likely to continue to be so. Road traffic laws and policies depend on a large number of factors. Making a correct decision for traffic management can be difficult because decision-makers need to analyze and absorb a large quantity of information. This information can be vague and sometime conflicting in nature. Therefore, there is a need for a
better control and a reliable and consistent system to help simplify the traffic decision making process. By incorporating the variables involved in traffic management such as the numbers of accidents, traffic violations, and traffic policemen on duty into an artificial intelligence technique, it is possible to build a traffic decision making system to help decision makers for analysis of traffic laws and policies. This paper proposes a decision support system using fuzzy logic to provide recommendations on whether traffic decision makers should, for example, give priority to increasing the number of traffic policemen on duty, increasing fines, or installing better/more safety devices. Fuzzy Logic allows us to measure imprecise and dynamic factors and to arrive at a reasonable judgment. A case study for Riyadh Region Traffic in Saudi Arabia has been analyzed using the proposed decision making system.
Index Terms:-Traffic Management, Decision support systems, Fuzzy logic.
INTRODUCTION:
Very year, lots of people die or suffer accidents because of factors that could have easily been prevented. The growing number of vehicles, ignorance of safety norms and reckless driving have attributed to the huge number of
Accidents. For example, in Saudi Arabia, there were 5168 fatalities in 2005 alone. This translates to 14 people died on roads in any given day. [1] The World Health Organization described traffic accidents as the "Epidemic of Civilized Societies". It is sad to note that these people just wanted to go home, visit a friend, or go to their work but encountered something terrible along the way. Although governments have existing traffic policies and continually strive to improve road safety, accidents always seem to happen.
THE PROBOSED DECISION SUPPORT MODEL:
The first and probably most important task in the process of designing a fuzzy logic decision support system is the identification of influencing parameters and to their relation on decision making process. In a traffic decision-making system, the traffic factors contributing primarily to a decision making and the typical traffic decisions are the fuzzy variables. After a discussion with number of personnel who had expert knowledge of road traffic in Riyadh Region Traffic decision-making, five influencing factors and four typical decisions were determined. All these factors and decisions are highlighted below in some detail.
Influencing Factors:
The first and most important factor is the number of traffic violations, which shows how many times traffic laws, has been broken. The number of traffic violations factor is considered as one available assessment criterion simply because of the fact that a large number of traffic violations would suggest that the traffic system was out of control, either because of a shortage of available personnel and equipment such as traffic policemen or traffic cameras, or because that the punishments and fines for having broken the law is not acting as a deterrent to drivers. The number of traffic accidents is a second important factor influencing the making of most traffic decisions making. It is a fact that reducing the number of traffic accidents is the principal goal of all traffic managements.
Typical Decisions:
There are a number of typical decisions made by traffic policy maker based on the identified influencing parameters. Launching a traffic awareness campaign is the first typical
decision. The wide concept underpinning traffic awareness campaigns is creating awareness about motorist and pedestrian safety, in order to minimize or prevent traffic accidents.
CONCLUSION:
Making the correct decision at the right time has been a major problem in road traffic managements, because decision-makers need to analyze and absorb a large quantity of information in a short time. The information can be vague and sometime conflicting in nature. This paper presented a fuzzy based decision support system to assist road traffic management. All parameters of the problem that was required to build the system have been defined and modeled using fuzzy sets. The traffic rules for the fuzzy inference system were extracted from traffic experts and historical data. In order to test and evaluate the proposed system, it has been demonstrated for a case study of Riyadh Region Traffic. The proposed system has shown that Fuzzy logic based approach has a considerable potential to be used in the development
of a road traffic management system to support decision-takers.