Integrated Optimization of Regional Signal Control and Traffic Assignment

To alleviate urban traffic congestion, reduce vehicle emissions' detrimental impact on the ecological environment, a dual-layer multi-objective programming model based on user equilibrium path choice model is proposed. The upper-layer model is a signal control optimization model that establishes an objective function with the total delay of traffic vehicles and CO emissions as the benefit indicators for the regional road network. The non-dominated sorting genetic algorithm is utilized to optimize signal control parameters such as signal cycle, effective green time for each phase, and phase difference, resulting in a new signal timing plan. The lower-layer model is a traffic flow allocation model aimed at finding a traffic flow distribution that satisfies user equilibrium, and it employs the Frank-Wolfe algorithm for solution. The interaction feedback between the upper and lower-layer models is implemented using MATLAB, and the feasibility of the models and solution algorithm is validated through case analysis. The experimental results demonstrate a 13.3% reduction in total delay for the regional road network and an 11.8% decrease in total emissions. The traffic conditions are improved, and operational efficiency is enhanced, leading to overall road network optimization.