Analyze traffic forecast for decentralized multi agent system using I-ACO routing algorithm

Abstract

Traffic congestion is a condition on road networks that may cause the vehicle to move very slow, longer trip timing changes, and vehicle queue length increase. We need accurate predictions which require accurate status information about vehicles—the fact that the vehicles are distributed over large-scale road infrastructure that makes mostly challenging one. Advanced vehicle guidance systems use real time traffic information but unfortunately can only react upon the presence of traffic. Anticipatory vehicle routing is promising approach, accounting for traffic forecast information. This concept presents an efficient decentralized approach for anticipatory vehicle routing that is particularly useful in large-scale dynamic environments with some additional techniques and experiments. The approach is based on delegate multi agent systems (MAS), i.e., an environment-centric coordination mechanism that is, in part, inspired by ant behavior. This paper mainly focus on provide the traffic forecast among the road network is very efficient to minimize the traffic.