Improving the capacity of intersections is the key to enhancing road traffic systems.Benefiting from the application of Connected Automated Vehicles(CAVs)in the foreseeing future,it is promising to fully utilize spati...Improving the capacity of intersections is the key to enhancing road traffic systems.Benefiting from the application of Connected Automated Vehicles(CAVs)in the foreseeing future,it is promising to fully utilize spatiotemporal resources at intersections through cooperative and intelligent trajectory planning for CAVs.Lane-free traffic is currently a highly anticipated solution that can achieve more flexible trajectories without being limited by lane boundaries.However,it is challenging to apply efficient lane-free traffic to be compatible with the traditional intersection control mode for mixed flow composed of CAVs and Human-driving Vehicles(HVs).To address the research gap,this paper proposes a spatiotemporal-restricted A∗algorithm to obtain efficient and flexible lane-free trajectories for CAVs.First,we restrict the feasible area of the heuristic search algorithm by considering the feasible area and orientation of vehicles to maintain the trajectory directionality of different turning behaviors.Second,we propose a spatiotemporal sparse sampling method by defining the four-dimensional spatiotemporal grid to accelerate the execution of the heuristic search algorithm.Third,we consider the motions of HVs as dynamic obstacles with rational trajectory fluctuation during the process of trajectory planning for CAVs.The proposed method can retain the advantage of efficiently exploring feasible trajectories through the hybrid A*algorithm,while also utilizing multiple spatiotemporal constraints to accelerate solution efficiency.The experimental results of the simulated and real scenarios with mixed flows show that the proposed model can continuously enhance traffic efficiency and fuel economy as the penetration of CAVs gradually increases.展开更多
Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Sinc...Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.展开更多
基金This work was supported by the Science and Technology Commission of Shanghai Municipality(Nos.22YF1461400 and 22DZ1100102)the National Key R&D Program of China(No.2022ZD0160104).
文摘Improving the capacity of intersections is the key to enhancing road traffic systems.Benefiting from the application of Connected Automated Vehicles(CAVs)in the foreseeing future,it is promising to fully utilize spatiotemporal resources at intersections through cooperative and intelligent trajectory planning for CAVs.Lane-free traffic is currently a highly anticipated solution that can achieve more flexible trajectories without being limited by lane boundaries.However,it is challenging to apply efficient lane-free traffic to be compatible with the traditional intersection control mode for mixed flow composed of CAVs and Human-driving Vehicles(HVs).To address the research gap,this paper proposes a spatiotemporal-restricted A∗algorithm to obtain efficient and flexible lane-free trajectories for CAVs.First,we restrict the feasible area of the heuristic search algorithm by considering the feasible area and orientation of vehicles to maintain the trajectory directionality of different turning behaviors.Second,we propose a spatiotemporal sparse sampling method by defining the four-dimensional spatiotemporal grid to accelerate the execution of the heuristic search algorithm.Third,we consider the motions of HVs as dynamic obstacles with rational trajectory fluctuation during the process of trajectory planning for CAVs.The proposed method can retain the advantage of efficiently exploring feasible trajectories through the hybrid A*algorithm,while also utilizing multiple spatiotemporal constraints to accelerate solution efficiency.The experimental results of the simulated and real scenarios with mixed flows show that the proposed model can continuously enhance traffic efficiency and fuel economy as the penetration of CAVs gradually increases.
基金supported in part by the National Natural Science Foundation of China(61603154,61773343,61621002,61703217)the Natural Science Foundation of Zhejiang Province(LY15F030021,LY19F030014)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(ICT1800407)
文摘Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.
