Trajectory optimization of vehicles at isolated intersection in a connected and automated environment
To enhance traffic efficiency at signal lights from the perspective of reducing vehicle de-lay and minimizing fuel consumption,this study proposes a vehicle platoon identification algorithm and a Multi-objective Linear Programming Trajectory Optimization Model for Platoon-leading Vehi-cles(MOLP-pl).First,the Intelligent Driver Model(IDM)is improved to adjust the vehicle state,re-duce the differences in vehicle speed and headway distribution under random arrival conditions,and provide a prerequisite for the operation of the subsequent MOLP-pl trajectory optimization model.On this basis,the vehicle platoon identification algorithm is utilized to discern the leading and fol-lowing vehicles,with the trajectory of the former serving as the optimization objective for establish-ing a corresponding mathematical model.Then,the vehicle trajectory optimization model is linear-ized and reconstructed to enhance efficiency and accuracy.Subsequently,a Liner Solver is employed to determine the acceleration of the leading vehicle,facilitating the construction of an optimal spatio-temporal trajectory.The IDM model is utilized to calculate the speed of the following vehicles.The simulation experiments conducted using SUMO demonstrate that:1)The vehicle trajectory optimiza-tion algorithm proposed in this study can significantly improve traffic efficiency at intersections.Un-der three different levels of traffic saturation,vehicle delay reduced by 8.56%,12.42%,and 64.79%,while fuel consumption decreased by 17.21%,18.34%,and12.64%,respectively,compared to the sce-nario without vehicle speed guidance;2)The vehicle delay reduced by-1.31%,2.63%,and 60.83%respectively,while the fuel consumption decreased by 2.47%,7.91%,and 2.28%in comparison to the logic-based control strategy.
intelligent transportationvehicle trajectory optimizationtraffic efficiency and consump-tionplatoon identificationSimulation of Urban MObility
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