A Signal Control Method for Bus Priority Considering the Delay of Non-priority Vehicles in a Connected-vehicle Environment

A connected-vehicle(CV)environment facilitates the collection of traffic data and the interactions among road users; therefore, it can contribute to more accurate evaluation of travel demand and traffic control. This paper investigates a signal control method at a single intersection for bus priority based on the weights for and delay distributions of bus and the other, non-priority vehicles. First, the arrival rates are calculated based on the trajectory data of connected buses and non-priority vehicles in the intersection, and the corresponding probability function of each phase is developed according to the distribution pattern of vehicle arrivals, based on which the probability of arrival rate is calculated using a maximum likelihood estimation model. Second, the wave speed of queuing, discharge, and departure are calculated respectively, using a traffic flow shock wave model. Third, the model specification for bus delay is carried out using the time-distance diagram of the shock-wave velocity, based on the fact that the number of buses in the traffic flow is less than regular vehicles while their weights are higher. Meanwhile, the model specification for non-priority vehicles is carried out using the Fixed Number Theory based on vehicles' arrival rate, considering the number of non-priority vehicles in traffic flow is larger while the weight of non-priority vehicle is lower, and most of them are not connected. The weighted delay of the intersection is calculated based on the number of passengers in vehicles. Finally, a mixed integer linear programming model is established to minimize the weighted delay, whose solution will then be used for optimizing signal control systems. To check the validity of the proposed method, a case study of the intersection of North Checheng Road and Dongfeng Avenue in the City of Wuhan is carried out. Traffic flow data of buses and non-priority vehicles at the intersection in different periods are collected, and an simulation experiment is accomplished based on Simulation of Urban Mobility(SUMO)Package. Experimental results show that the average delays for buses reduce by 25.63%, 25.25%, and 18.32%, under the scenario of low, medium, and high traffic flow rate, respectively. Compared with those before optimization, the average delays for non-priority vehicles in a single cycle under the same scenarios reduce by 8.80%, 4.68%, and 1.99%, respectively; and the average weighted delay in a single cycle under the same scenarios reduce by 20.98%, 9.39%, and 12.70%, respectively. The above results show that the proposed method is suitable and performs better in different traffic settings.