Abstract:

To improve efficiency and deal with uncertainty of intelligent signal control for mixed traffic flow at signalized adjacent road intersections in cities, a robust multi-objective optimization control model is established to deal with uncertainty due to cycle length disturbance and traffic flow fluctuation. An intelligent optimization control method based on robust multi-objective evolutionary algorithm, IDR-NSGA-II, is proposed. Key techniques are improved, including adaptive sampling, definition of robustness degree and robust partial order relation, for better performance and running speed of the algorithm. A new multi-attribute decision making analysis method, ELM-MADMA, is presented to select a satisfactory solution from the Pareto front set. Simulation of an adjacent intersection in Shanghai shows that the proposed control method can simultaneously optimize several traffic performance indicators such as vehicle average delay, road capacity, chronic traffic average delay and average parking rate of motor vehicle under cycle length disturbance and traffic flow fluctuation. The ELM-MADMA algorithm outperforms other decision making methods, with improved system efficiency.

Key words: intelligent traffic signal control, robust multi-objective optimization, adjacent intersection, mixed traffic flow, multi-objective optimization control