Study On Green Time Optimization Model Of Signalized Intersection Under Random Conditions

With the development of urbanization,the problem of urban road traffic congestion becomes more and more serious.As an important node that affects the efficiency of urban traffic operation,the rationality of signal control method and the effectiveness of signal timing scheme are very important to optimize the overall operation efficiency of urban traffic.According to the stochastic characteristics of traffic flow,this paper optimizes the signal timing model,studies the adaptive signal control method of single intersection of urban road,establishes the adaptive green time optimization model by using the theory and method of chance constrained programming,and obtains the signal timing scheme of intersection based on the adaptive particle swarm optimization algorithm.Based on the previous research of scholars,this paper has carried out the following work:(1)This paper reviews the methods of dealing with the random characteristics of traffic flow in the previous studies,expounds the intersection signal control mode and adaptability,introduces the basic parameters of intersection signal control and gives its traditional calculation methods.(2)The adaptive green time optimization model based on the minimum green deviation value is established,and the method of optimizing the green time and calculating the signal timing scheme with the minimum green deviation value as the optimization objective is obtained.According to the characteristics of the green deviation value,the improved model of secondary adjustment of the green time of each phase is derived.The dynamic traffic parameters of the adjacent signal periods of intersections are established.In this paper,an adaptive green time optimization model based on two-stage stochastic programming is proposed to optimize the timing scheme of each signal period.The feasibility of each model is verified by simulation,and the applicability of the proposed adaptive signal timing model is determined.(3)The principle and algorithm flow of the basic particle swarm optimization(PSO)algorithm and the adaptive particle swarm optimization(APSO)algorithm are sorted out.Combined with the uncertainty of the arrival rate of the traffic flow,the chance-constraint planning is added to the constraint conditions and the penalty function method is used to deal with it.The inertia weight and learning factor are adaptively set to avoid the phenomenon of the algorithm’s reduced search range and poor accuracy in the later stage.The green time optimization model,the improved model and the two-stage adaptive green time optimization model based on the adaptive particle swarm optimization algorithm are proposed.(4)This paper summarizes several adaptive signal timing models proposed in this paper,compare the control benefits of different types of signal timing models under different arrival rate fluctuation conditions,and get the most suitable signal timing scheme for the current traffic state.Based on this,we propose an adaptive signal timing model selection strategy based on the arrival rate characteristics of key phase vehicles.This strategy ensures that the signal timing scheme designed by the model can always maintain good control benefits for the intersection regardless of the fluctuation state of traffic flow parameters.By comparing the strategy of this paper with the adaptive control strategy based on single model in the previous research,it is proved that the control efficiency of this strategy is better than that of the adaptive control strategy based on single model under different phase arrival rate variance,and the effectiveness of the adaptive signal control model selection strategy based on this model is verified.