A multi-agent based cellular automata model for intersection traffic control simulation

Abstract

The transportation system is a complex system with multiple transportation elements. Therefore, how to simulate a complex transportation system is a difficult point in transportation research. The complex element in transportation can be regarded as a multi-agent system, based on this, this paper proposes a multi-agent based cellular automata model. Applying multi-agent theory to simulate complex elements in traffic, and due to the simplified rules and high simulation efficiency of cellular automata, the model can efficiently simulate complex traffic scenes. In the model, car-following and lane-changing rules applied to different traffic scenarios are proposed, and the model is used to simulate the fixed time strategy and induction control strategy of road intersections. The simulation results show that the traffic control effect under the induction control strategy is better than the traditional fixed time control strategy. In the induction control strategy, the average speed of the vehicle is faster, the travel time and the queue length are shorter, and the road congestion problem caused by saturated traffic volume can be effectively avoided.