Green Energy and Intelligent Transportation最新文献

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.

Advanced Air Mobility (AAM) has emerged as a pioneering concept designed to optimize the efficacy and ecological sustainability of air transportation. Its core objective is to provide highly automated air transportation services for passengers or cargo, operating at low altitudes within urban, suburban, and rural regions. AAM seeks to enhance the efficiency and environmental viability of the aviation sector by revolutionizing the way air travel is conducted. In a complex aviation environment, traffic management and control are essential technologies for safe and effective AAM operations. One of the most difficult obstacles in the envisioned AAM systems is vehicle coordination at merging points and intersections. The escalating demand for air mobility services, particularly within urban areas, poses significant complexities to the execution of such missions. In this study, we propose a novel multi-agent reinforcement learning (MARL) approach to efficiently manage high-density AAM operations in structured airspace. Our approach provides effective guidance to AAM vehicles, ensuring conflict avoidance, mitigating traffic congestion, reducing travel time, and maintaining safe separation. Specifically, intelligent learning-based algorithms are developed to provide speed guidance for each AAM vehicle, ensuring secure merging into air corridors and safe passage through intersections. To validate the effectiveness of our proposed model, we conduct training and evaluation using BlueSky, an open-source air traffic control simulation environment. Through the simulation of thousands of aircraft and the integration of real-world data, our study demonstrates the promising potential of MARL in enabling safe and efficient AAM operations. The simulation results validate the efficacy of our approach and its ability to achieve the desired outcomes.