Multi-agent System based Urban Traffic Management

Abstract

Road Traffic congestion can occur anywhere from normal city roads, freeways to even highways. Traffic congestion can also be accentuated by incidents like terrorist attacks, accidents and breakdowns. This paper summarizes the use of various evolutionary techniques for traffic management and congestion avoidance in Intelligent Transportation Systems. Evolutionary algorithms with their inherent strength as optimization techniques are good candidates for solutions to road traffic management and congestion avoidance problems. A number of approaches involving the use of Genetic algorithms, Learning Classifier Systems and Genetic programming have been discussed for solutions to different problems in this domain. This paper proposes a multi-agent based real-time centralized evolutionary optimization technique for urban traffic management in the area of traffic signal control. This scheme uses evolutionary strategy for the control of traffic signal. The total vehicle mean delay in a six junction network was reduced by using evolutionary strategy. In order to achieve this the green signal time was optimized in an online manner. Comparison with a fixed time based traffic controller has been made and was found to produce better results.