Research on Max Pressure Signal Control Method for Collaborative Optimization of Motor Vehicles and Non-Motor Vehicles

Abstract

In addressing the shortcomings of contemporary traffic signal controls, which often neglect non-motor vehicles, a novel strategy has been introduced. This strategy, grounded in the max pressure control algorithm, aspires to cooperatively optimize the movement of both motor and non-motor vehicles, its implementation aims to enhance travel service efficiency while mitigating potential queue congestions. Initially, vehicle arrival rates at the intersection downstream is determined by using the vehicle arrival rate at the intersection upstream, along with the Robertson’s platoon dispersion model. Afterwards, we examine the total delays encountered by both motor vehicles and non-motor vehicles at different levels of traffic congestion, depending on their distinct arrival scenarios at the downstream intersections. Then, the queue length of the downstream intersection is predicted based on the Robertson’s platoon discrete model. Relying on the characteristics of the max pressure control algorithm, the feedback optimization of the green time of each phase is carried out in combination with the queue length to achieve the maximization of the traffic travel service while effectively preventing the queue overflow. We utilized SUMO to construct a simulation environment in order to verify the effectiveness of the optimization model, using Beijing Road in Kunming as a case study. The validation results indicate that incorporating both motor and non-motor vehicles in the max pressure signal control significantly enhances the efficiency of traffic travel service. This approach outperforms the fixed signal timing method proposed by Webster and the traditional inductive signal control. It maintains order in the road network during oversaturated conditions effectively and rapidly, prevents queue overflow, and achieves higher traffic throughput. Furthermore, compared to the max pressure signal control that only considers motor vehicles, the queue lengths for both motor and non-motor vehicles are reduced by 8.84% and 15.82% respectively, resulting in an 11.11% decrease in average intersection delay.

Keywords: heterogeneous traffic, signal collaborative optimization, max pressure signal control, delay, queue length, offset