Research On Variable Speed Limit Control Of Freeway Under Mixed Traffic Flow

As an essential carrier of traffic between cities,the freeway is an integral part of China’s road transportation.With the rapid development of China’s economy,the number of freeway passenger and cargo vehicles has been increasing year by year,triggering the increasingly prominent problem of congestion on bottleneck sections.In recent years,along with the development of artificial intelligence,intelligent vehicles are gradually entering into daily life,facilitating people’s travel and bringing new solutions to the freeway congestion problem.Therefore,for the mixed traffic flow environment,the variable speed limit control strategy is proposed,and it is important to study the variable speed limit control under mixed traffic flow for the bottleneck area congestion problem.Firstly,the bottleneck area of the freeway is taken as the research object,the definition of bottleneck area and its classification are explained,and the causes of the formation of freeway bottleneck area are analyzed to explore the mechanism of congestion in this area.On this basis,the theory of variable speed limit is explored,and the superiority of variable speed limit over the fixed speed limit is verified through simulation;the characteristics of intelligent networked vehicles under mixed traffic flow are clarified,and the characteristics of mixed traffic flow are analyzed and discussed.Secondly,through the comparative analysis of traffic flow models,the METANET model is selected as the basic traffic flow model for this study,and the model establishment process is derived;the model is improved under the careful consideration of the mutual influence between different types of vehicles in mixed traffic flow and the influence factors of upstream road sections;through the simulation,the model under mixed traffic flow is calibrated with parameters,and through the comparison and analysis with The validity of the proposed model is verified by comparison and analysis with traffic flow data.Again,the variable speed limit control process is elaborated based on the prediction framework of the model;the control strategy with multiple objectives is determined by considering the aspects of traffic efficiency,driving comfort,environmental benefits,and economic benefits,and the weights of each control objective are determined by normalization and hierarchical analysis;the constraints are defined for the objective function by considering driving safety and driving experience;the sparrow search algorithm is clarified as the solution method,and its effectiveness is verified by comparing with the traffic flow data.As the solution method,its basic principle and parameter setting results are explained.Finally,SUMO is selected as the simulation platform to build a road simulation environment,and the built-in software is modified to change the vehicle settings to simulate an intelligently networked vehicle,and then the traffic control interface(Tra CI)in SUMO is used to conduct joint simulation with MATLAB software to realize variable speed limit control under mixed traffic flow.The analysis and comparison of simulation results prove that the variable speed limit control strategy for freeways under mixed traffic flow proposed in this study has a good control effect on the bottleneck area,and this control strategy can improve the efficiency of the freeway bottleneck area and relieve traffic congestion;meanwhile,as the percentage of intelligent network-connected vehicles in the traffic flow increases,the effect on the control strategy is also significantly improved.