Trajectory-driven planning of electric taxi charging stations based on cumulative prospect theory. (November 2022)

• Record Type: Journal Article
• Authors: Li, Yujing; Su, Su; Liu, Biao; Yamashita, Koji; Li, Yan; Du, Liang
• Is Part Of: Sustainable cities and society. Volume 86(2022)
• Publication Date: 2022-11
• Subjects: Electric taxi -- Charging station -- Trajectory data -- Charging simulation algorithm -- Charging decision-making -- Cumulative prospect theory; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2022.104125
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Trajectory data provides a new perspective for studying charging station planning. The peak charging time for ETs is in the afternoon. ET drivers' attitude toward the risk of battery depletion influences the charging decision. Results confirm correlation between charging station planning and users' charging behavior decisions. Simulation results show the impact of EV charging demand by the user's per-unit time cost. Abstract: The charging station planning is crucial to facilitate convenient and efficient charging of electric taxis (ETs). As typical transportation and mobile power load, the accurate modeling of driving and charging behaviors of ETs is the basis for charging station planning. Therefore, this paper proposes a joint modeling approach for user charging behavior decision-making and charging station planning. First, the open-source taxi trajectory dataset is used to characterize the driving behavior of ETs. The temporal and spatial distribution characteristics of passenger trips can be obtained through data mining and modeling. Then, an ET charging demand simulation model based on cumulative prospect theory (CPT) is developed. The CPT captures individuals' attitudes and preferences against the battery-depleting risk in decision-making. By analyzing the operating characteristics of ETs, a more practical charging simulation algorithm is developed. Finally, the joint modeling of user charging behavior decisions and charging station planning is realized to elucidate their interplay. Case studies are conducted in a practical area in Xi'an, China. The results show that the layout of charging stations and the driver's per-unit time cost influence the charging decisions of ET drivers. The proposed approach models the interaction mechanism between traffic and electrical attributes of ETs, user behavior decision-making, and charging station planning, which is significantly lacking in existing studies. The proposed approach is applicable to the problem of planning charging stations in urban areas.