Research On Traffic Capacity Of Signalized Intersection In Mobile Internet Environment

The traffic flow at signalized intersections evolves into a stop-and-go operating state due to the control of signal lights,this state leads to low average vehicle speeds and large traffic delays,which in turn affects the operating efficiency of the entire road network level.The capacity of signalized intersections directly determines the operating status of the entire city,but there is currently no effective scientific method to improve the capacity of signalized intersections in the short term.The growing maturity of the Internet of Vehicles technology has brought new opportunities for the improvement of the traffic capacity of signalized intersections in the short term.Therefore,based on the Internet of Vehicles technology,this article deeply analyzes the factors that affect the traffic capacity of signalized intersections in a mixed traffic flow environment,proposes a pre-stop line vehicle guidance strategy,and studies the pre-stop line vehicle guidance through theoretical analysis and numerical experiments.The state of traffic flow at signalized intersections under the driving strategy,and the potential impact of this strategy on vehicle fuel consumption is discussed.The specific research work is as follows:(1)Through field investigation of traffic flow characteristics and capacity at signalized intersections,a pre-stop line vehicle guidance strategy is proposed.Under the established signal timing and traffic geometric characteristics,the measured data is used to in-depth analysis of the traffic flow characteristics and capacity at the intersection,derive the main influencing factors of the capacity at the signalized intersection(average time headway and start-up acceleration time loss of vehicles),and propose a pre-stop line vehicle guidance driving strategy to reduce the average headway of vehicles driving at intersections.With the principles of guiding and standardizing vehicle driving behavior,the signal timing algorithm which meets the actual signal timing is designed,and under the pre-stop line environment the main pre-signal is synergistically optimized.Both theoretical analysis and numerical experiments show that the pre-stop line vehicle guidance driving strategy can effectively improve the traffic capacity of signalized intersections.(2)Based on the Internet of Vehicles environment,a new micro car-following model considering speed guidance technology is proposed for the pre-stop line vehicle guidance driving strategy.The new model considers the influence of the vehicles’ communication technology on the traffic capacity of signalized intersections on the basis of the pre-stop line vehicle guidance driving strategy,reveals the adaptive control mechanism of signalized intersections in the Internet of Vehicles environment and the basic characteristics of traffic flow operation,and analyzes the influence of speed guidance technology on the car-following behavior of mixed traffic flow.Simulation experiments prove that this method has a positive effect on the traffic capacity of signalized intersections,and it can be implemented in the case of low penetration of connected vehicles.(3)According to the characteristics of vehicle fuel consumption at signalized intersections,based on the pseudo-spectral solution method,an optimal control model targeting the minimum vehicle fuel consumption is proposed.And with the characteristics of vehicle driving state changes at signalized intersections,a multi-stage optimal control model is proposed.The fuel consumption of vehicles driving in the reserved area is separately optimized to obtain a suitable reserved area length and signal timing,and verify through a multi-stage optimal control model to ensure that the collaborative optimization of overall traffic capacity of the signalized intersection and vehicle fuel consumption is achieved.The proposed multi-stage optimal control model is compared with the optimal control model of other scholars,and the simulation experiment shows that the optimal control model under the pre-stop line vehicle guidance driving strategy can better realize the collaborative optimization of intersection capacity and vehicle fuel consumption under the low penetration rate of intelligent vehicles.