Traffic Congestion Detection and Avoidance by Finding Optimal Path in VANET

Abstract

A Vehicular Ad-hoc Networks are self-organizing networks established among vehicles equipped with communication facilities. Due to recent advancements in vehicular technologies vehicular communication has emerged. For a rich set of applications implementing Intelligent Highways, like application related to road safety, traffic monitoring and management, road disaster mitigation etc. The road side infrastructure plays a vital role for any VANET. This is the reason that efficient communication between the vehicles and the road side infrastructure is required. Meeting this requirement becomes very difficult as nodes in a VANET are highly mobile and thus the network topology is highly dynamic. In such a state of affairs it is required to optimize the vehicle to infrastructure communication to achieve better efficiency. The objective of work is to detect traffic congestion and avoid it by finding the optimal path and give the details of all available path to the user. In this thesis, approach for finding congestion of vehicle due to overloaded traffic on the road and also suggest optimal path for avoiding traffic congestion. The proposed algorithm can reduce the traffic congestion by giving routing suggestion to new arrival vehicle, by redirecting it on the road which contain less vehicle density. We develop Car Agent (CA) and Signal Agent(SA) for Traffic Congestion detection and avoidance by suggesting optimal path to vehicle. Simulation results shows that proposed approach reduce traffic congestion of vehicles on road and also give reduction in individual trip time.