Analysis of Spatiotemporal Distribution Characteristics of Charging Demand Based on Electric Taxi Trajectory Data

Under the background of 'Dual carbon' goals and taxi electrification, issues such as the mismatch between charging supply and demand in time and space have emerged. In this study, Beijing electric taxis are selected as the study subject. The long-stay detection algorithm is designed based...

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Bibliographic Details
Published in2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE) pp. 1 - 7
Main Authors Wang, Jiamei, Wang, Pinxi, Huang, Ailing
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.03.2025
Subjects
charging demand identification
Control engineering
Detection algorithms
Dispersion
electric taxis
Electrification
Layout
Public transportation
spatiotemporal characteristics
Spatiotemporal phenomena
Supply and demand
Trajectory
trajectory data
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Summary:Under the background of 'Dual carbon' goals and taxi electrification, issues such as the mismatch between charging supply and demand in time and space have emerged. In this study, Beijing electric taxis are selected as the study subject. The long-stay detection algorithm is designed based on trajectory data to identify charging behavior. The Natural Breaks method is employed to divide grid functional attributes, which is beneficial for accurately analyzing the spatiotemporal distribution characteristics of charging demand. The results show that charging demand is concentrated after the morning and evening peaks and during meal breaks both on weekends and weekdays. The starting SOC for charging is mostly between 20% and 60%, while the termination SOC is concentrated between 70% and 100%. In different functional areas, charging demand exhibits distinct spatiotemporal patterns. On weekends, demand is more scattered, with noticeable early morning charging. On weekdays, a clear double-peak pattern is observed. In office and transport areas, peaks occur during commuting hours and lunch break. In mixed-use areas, charging demand peaks around 10:00 and 20:00. Commercial areas show scattered and fluctuating charging patterns. In living and residential areas, charging demand is concentrated at night and before the morning peak. Scenic areas experience charging peaks after park entry and closure. The findings will offer valuable insights for optimizing the layout of charging infrastructure.
DOI:10.1109/ICAACE65325.2025.11019504