Agent Based Road Traffic Congestion Management Using Vehicle Horn Model

Abstract

Traffic congestion is one of the most important problems we face in our daily life. This problem has a huge effect on economy and environment. The total of congestion cost in America in 2010 reached to 101 billion dollar and it is expected to reach to 175 billion dollar in 2020 [1]. Despite of many research in this field the problem still exists all over the world. The new domain for solving this problem is using agent based technology, which can model the traffic network as agents, these agents can communicate, cooperate to achieve the optimal performance of the network. Our thesis proposed a solution for this problem based on agent technology. We use various agents in our model which include lane agent, vehicle agent and traffic light agent. We use lane agent for every lane of the network to compute the weight of the lane and send it to traffic light agent. This weight depends on a new model called horn model. The horn is the sound produced by vehicles to give an indication that green sign is needed, this sound increase with waiting time and decrease as the distance of waiting vehicles from traffic light increase . . Traffic light agents use the congestion factor and weight it receive from lane agents to optimize traffic light signal. Optimization is done by giving the needed time for every lane according to the number of waiting vehicles and to keep track of the waiting time of other vehicles in other lanes by using lane weight. This solution can give a dynamic cycle time according to real time traffic data. Route guidance system based on congestion factor is used also to stop sending vehicles to congestion area. Traffic light agents communicate to ii