Multi-agent systems for simulating traffic behaviors

Multi-agent systems allow the simulation of complex phenomena that cannot be easily described analytically. As an efficient tool, the agent-oriented traffic models have emerged to investigate vehicular traffic behaviors. In this article, a new agent-based traffic simulation model is proposed for solving the traffic simulation problems. A vehicle with the driver is represented as a composite autonomous agent in this model. Different from the classical car-following principles, vehicle-agent moving approaches are proposed instead of particle-hopping techniques. This model defines reasonable acceleration and deceleration rates at any certain condition. In this simulation, this can offer a natural, even cognitive way to follow the leader and switch lane. The simulation results have verified that this model has achieved more realistic results without loss of accuracy than those obtained from the cellular automata traffic models. This model can provide better simulation performance than the traditional vehicle-following models which are used to investigate the nonequilibrium traffic flow. A comparison of the real flow with the simulated freeway flow and lane capacity highlights the validness of this model.