Traffic Signal Optimization Based on System Equilibrium and Bi-level Multi-objective Programming Model

Abstract

Based on the design principle of minimizing the automotive exhaust emissions and the total impedance of the road network, an urban road traffic signal control method of bi-level multi-objective programming model is established by designing the heuristic particle swarm optimization (PSO) algorithm. First of all, the upper-level model which combined the vehicle emissions model and system optimum assignment model is built, and the lower-level model is built based on minimizing the sum of the link travel time function integral. Then, the heuristic PSO algorithm is designed and transformed to solve upper-level and lower-levels model iteratively by two PSO algorithms. Ultimately, by altering the weight parameters of the upper model, the model is dealt with separately in case of single target and multi-target, the optimization results of which is compared with the VISSIM simulation results and the optimization results by means of heuristic genetic algorithm. The simulation results show that bi-level multi-objective control method, which could improve the operating quality of road network, is of great optimization ability and can effectively reduce the automotive exhaust emissions and the total impedance of the road network.