基于交通流预测和实时交通事件的最优电动汽车路径规划

Meriem Sebai, Lilia Rejeb, Mohamed Ali Denden, Yasmine Amor, Lasaad Baati, Lamjed Ben Said
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引用次数: 2

摘要

电动汽车(ev)被认为是最环保、最经济的交通解决方案之一。然而,障碍和范围限制阻碍了这项技术的进步和部署。本文通过分析历史轨迹数据,对电动汽车的路线规划进行研究,得出考虑能耗的最优路线。更具体地说,我们提出了一种新的电动汽车路线规划方法,该方法考虑了实时交通事件、道路拓扑、电池故障时充电站的位置,最后,从历史轨迹数据中提取交通流预测来生成能量图。我们的方法包括四个阶段:离线阶段,旨在构建能量图,应用A*算法提供最优的电动汽车路径,NEAT轨迹聚类,旨在生成一天中给定时段的密集轨迹聚类,最后是基于我们的算法的在线阶段,基于真实交通事件,密集轨迹聚类,道路拓扑信息,车辆特征和充电站位置规划最优的电动汽车路径。通过实例实验建立了电动汽车的最优路径,验证了算法的有效性和高效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Electric Vehicles Route Planning with Traffic Flow Prediction and Real-Time Traffic Incidents
Electric Vehicles (EVs) are regarded to be among the most environmentally and economically efficient transportation solutions. However, barriers and range limitations hinder this technology’s progress and deployment. In this paper, we examine EV route planning to derive optimal routes considering energy consumption by analyzing historical trajectory data. More specifically, we propose a novel approach for EV route planning that considers real-time traffic incidents, road topology, charging station locations during battery failure, and finally, traffic flow prediction extracted from historical trajectory data to generate energy maps. Our approach consists of four phases: the off-line phase which aims to build the energy graph, the application of the A* algorithm to deliver the optimal EV path, the NEAT trajectory clustering which aims to produce dense trajectory clusters for a given period of the day, and finally, the on-line phase based on our algorithm to plan an optimal EV path based on real traffic incidents, dense trajectory clusters, road topology information, vehicle characteristics, and charging station locations. We set up experiments on real cases to establish the optimal route for electric cars, demonstrating the effectiveness and efficiency of our proposed algorithm.
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