Nowadays, traffic jam is a rising and tremendous issue, particularly in big cities. Adding and controlling the traffic signal has been an effective way to mitigate this problem. More effectively, we can adjust the signal plan for each junction on the traffic network in dynamic. Many work in literature tried this effort to control the traffic signal with different optimization methods. Among them, Particle Swarm Optimization (PSO) algorithm is a popular optimization method for its simple concepts, little parameter adjustment, high calculation speed, and strong global search capability. In this paper, we model the online traffic network with Cell Transmission Model (CTM), and then apply the sequential PSO algorithm to optimize the control of the traffic signal network. Our objective is to minimize the overall delay on the traffic network and reduce the fuel consumption. The evaluative results show that, compared with SGA algorithm and PPSO algorithm, the TSCOPSA algorithm has the fastest convergence rate and the best performance. Meanwhile, the result of TSCOPSA algorithm has the most accuracy among all three algorithms.
['Bin Wu', 'Dong Wang']
Traffic signal networks control optimize with PSO algorithm