Traffic Signal Control In Incomplete Connected Vehicles Environment

With the rapid economic development in our country,the number of motor vehicles has been increasing year by year and traffic problem has become more and more serious.Traffic signal control as the core content of intelligent transportation system(ITS)has become an efficient means to solve traffic problems.Meanwhile,the rapid development of connected vehicles has provided a great deal of real-time and accurate traffic data for traffic signal control,which brings opportunities and challenges to real-time signal control.At the same time,there will be a long transition period where only a portion of vehicles equipped connected device and how to make better use of the part of connected vehicles data to improve the signal control effect is an urgent problem to be solved.Based on the research of traffic state estimation under incomplete connected vehicle environment,this paper studies the real-time single intersection signal control and real-time arterial signal control.The main contributions of this paper are summarized as follows:(1)Propose a traffic status estimation method in incomplete connected vehicle environment based on partial connected vehicles data.Aiming at the scenario of low penetration rate,the offline traffic parameters are estimated and a trajectory-based traffic flow estimation method is proposed for off-line signal timing optimization.In the scenario of high penetration rate,the on-line traffic state parameter estimation is studied.An algorithm esimating the location and speed of unconnected vehicles with car following model is proposed.Based on this,the estimation of queue length and platoons identification are carried out and the evaluation of the estimation algorithm is performed by TransModeler simulation platform,which lays the foundation for the subsequent real-time adaptive traffic signal control.(2)Based on the second chapter of traffic state estimation,a single-intersection real-time signal control algorithm is proposed.By introducing the NEMA double loop structure,the dynamic programming method is used to realize the optimization of phase sequence.Finally,simulation is carried out on the connected vehicle platform built by TransModeler simulation software to compare the control effects under different penetration rates.Comparative analysis proves the applicability of the algorithm under a certain penetration rates.(3)On the basis of the second chapter of the platoon identification,this paper proposes an artery dynamic coordination control algorithm.By constructing a mixed integer programming model based on platoons,the algorithm optimize the signal timing parameters of artery intersections with the minimum total delay of the patoons at artery intersections.A dynamic coordination of the artery is achieved with the algorithm.At the same time,the segment capacity constraints and selection variables are introduced for the prevention of queue overflow and phase sequence optimization.Finally,the simulation case is compared with the traditional artery coordination algorithm to prove the better applicability of the algorithm in stochastic traffic flow.Meanwhile,simulation experiments is carried out under different penetration rate and the scope of the algorithm is given.