出租车轨迹数据驱动的充电站选址定容方法

doi:10.16097/j.cnki.1009-6744.2025.05.026

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (5): 291-301.DOI: 10.16097/j.cnki.1009-6744.2025.05.026

出租车轨迹数据驱动的充电站选址定容方法

张文会^*，乔梓凡，陈德启

东北林业大学，土木与交通学院，哈尔滨150040

收稿日期:2025-07-04 修回日期:2025-08-21 接受日期:2025-08-25 出版日期:2025-10-25 发布日期:2025-10-25
作者简介:张文会(1978—)，男，黑龙江哈尔滨人，教授，博士。
基金资助:
国家自然科学基金(52572369)；黑龙江省哲学社会科学研究规划项目(23GLCo22)。

Charging Station Location and Capacity Determination Algorithm Based on Taxi Trajectory Data

ZHANG Wenhui^*, QIAO Zifan, CHEN Deqi

School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China

Received:2025-07-04 Revised:2025-08-21 Accepted:2025-08-25 Online:2025-10-25 Published:2025-10-25
Supported by:
National Natural Science Foundation of China (52572369)；Heilongjiang Province Philosophy and Social Science Research Planning Project (23GLCo22)。

摘要/Abstract

摘要： 针对电动汽车充电设施供需匹配失衡问题，本文融合出租车动态与静态轨迹数据，采用能耗估计模型建立荷电状态(SOC)估计方法，分析包含SOC和停留时间轨迹点的潜在充电需求，运用帕累托最优原理构建多准则决策函数，识别具有显著充电需求的短时间停留点。采用基于密度的空间聚类算法(DBSCAN)进行空间分析，将得到的聚类中心作为候选点；采用排队模型模拟电动汽车充电过程，确定充电桩数量。构建充电站选址多目标规划模型，设计蚁群算法(ACO)与遗传算法(GA)混合求解策略，引入兴趣点(POI)权重和土地标定价格构建双重启发函数。以深圳市出租车轨迹数据作为算例，获得城市充电站数量为40座，社会总成本为3539.40万元。研究结果可为城市电动汽车充电站选址优化与定容提供理论依据。

关键词: 交通工程, 选址定容规划, 轨迹数据驱动, 公共充电站, 充电需求

Abstract: To address the imbalance between the supply and demand of electric vehicle (EV) charging facilities, this study integrates both dynamic and static trajectory data of taxis, and employs an energy consumption estimation model to establish a state-of-charge (SOC) estimation method. By analyzing trajectory points containing SOC and dwell time information, the potential charging demand is identified. A multi-criteria decision-making function is constructed based on the Pareto optimality principle to recognize short-term dwell points with significant charging demand. The density-based spatial clustering algorithm (DBSCAN) is applied for unsupervised spatial analysis, using the resulting cluster centers as candidate locations for charging stations. A queuing model is used to simulate the EV charging process and determine the required number of charging piles. Subsequently, a multi-objective optimization model for charging station siting is formulated, and a hybrid solution strategy is designed combining the Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Dual heuristic functions are introduced by incorporating Point of Interest (POI) weights and land calibration prices. Using taxi trajectory data from Shenzhen city as a case study, the optimal plan yields 40 charging stations with a total social cost of 35.394 million yuan. The research results provide a theoretical basis for optimizing the siting and capacity planning of urban EV charging stations.

Key words: traffic engineering, site selection and capacity planning, trajectory data-driven, public charging station, charging demand

中图分类号:

U491

张文会, 乔梓凡, 陈德启. 出租车轨迹数据驱动的充电站选址定容方法[J]. 交通运输系统工程与信息, 2025, 25(5): 291-301.

ZHANG Wenhui, QIAO Zifan, CHEN Deqi. Charging Station Location and Capacity Determination Algorithm Based on Taxi Trajectory Data[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(5): 291-301.

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出租车轨迹数据驱动的充电站选址定容方法

Charging Station Location and Capacity Determination Algorithm Based on Taxi Trajectory Data

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