Cooperative control of multiple intersections combining agent and chaotic particle swarm optimization

•We constructed the chaotic particle swarm optimization algorithm by combining chaos algorithm and agent interactive and cooperative control technology.•Multi-agent is applied to the collaborative control of intersections to realize the collaborative decision-making of multi-intersections.•We defined the state and action of intersection signal control agent to reduce the dimension of state-action space and speed up the convergence rate of iteration so as to find the optimal solution more efficiently.•We proposed a method takes vehicle delay, vehicle queue length and regional access ratio as optimization objectives, implements regional traffic signal control strategy and improves overall traffic efficiency. The regional intersection can be regarded as an agent model, that should be controlled and optimized. The traditional particle swarm optimization algorithm has been enhanced through optimization techniques and the inclusion of chaos algorithms and agent interactive and cooperative control technology, resulting in the development of a chaotic particle swarm optimization algorithm. Multi-agent is applied to the collaborative control of intersections to realize the collaborative decision-making of multi-intersections. The state and action of intersection signal control agent are defined to reduce the dimension of state-action space and speed up the convergence rate. The proposed method takes vehicle delay, vehicle queue length and regional access ratio as optimization objectives, implements regional traffic signal control strategy and improves overall traffic efficiency. By constructing a simulation environment based on Paramics software, the effectiveness of the regional traffic signal control method based on chaotic particle swarm optimization algorithm is verified, and the convergence efficiency and signal timing optimization have interactive effects. [Display omitted]