Multi-Agent and Fuzzy Inference-Based Framework for Traffic Light Optimization

Abstract

Despite the fact that agent technologies have widely gained popularity in distributed systems, their potential for advanced management of vehicle traffic has not been sufficiently explored. This paper presents a traffic simulation framework based on agent technology and fuzzy logic. The objective of this framework is to act on the phase layouts represented by its sequences and length to maximize throughput and fluidize traffic at an isolated intersection and for the whole multi-intersection network, through both inter- and intra-intersection collaboration and coordination. The optimizing of signal layouts is done in real time, and it is not only based on local stream factors but also on traffic stream conditions in surrounding intersections. The system profits from agent communication and collaboration as well as coordination features, along with decentralized organization, to decompose the traffic control optimization into subproblems and enable the distributed resolution. Thus, the separate parts can be resolved rapidly by parallel tasking. It also uses fuzzy technology to handle the uncertainty of traffic conditions. An instance of the proposed framework was validated and designed in the ANYLOGIC simulator. Instantiation results and analysis denote that the designed system can significantly develop the efficiency at an individual intersection as well as in the multi-intersection network. It reduces the average travel delay and the time spent in the network compared to multi-agent-based adaptative signal control systems.