Trajectory-driven planning of electric taxi charging stations based on cumulative prospect theory
•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’...
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| Veröffentlicht in: | Sustainable cities and society Jg. 86; S. 104125 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.11.2022
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| ISSN: | 2210-6707, 2210-6715 |
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| Abstract | •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.
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. |
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| AbstractList | •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.
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. |
| ArticleNumber | 104125 |
| Author | Li, Yujing Yamashita, Koji Liu, Biao Du, Liang Li, Yan Su, Su |
| Author_xml | – sequence: 1 givenname: Yujing surname: Li fullname: Li, Yujing organization: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China – sequence: 2 givenname: Su surname: Su fullname: Su, Su email: ssu@bjtu.edu.cn organization: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China – sequence: 3 givenname: Biao surname: Liu fullname: Liu, Biao organization: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China – sequence: 4 givenname: Koji orcidid: 0000-0002-1892-2455 surname: Yamashita fullname: Yamashita, Koji organization: Electrical and Computer Engineering, University of California Riverside, CA 92521, United States – sequence: 5 givenname: Yan surname: Li fullname: Li, Yan organization: Department of Electrical Engineering, The Pennsylvania State University, University Park, PS 16802, United States – sequence: 6 givenname: Liang surname: Du fullname: Du, Liang organization: Department of Electrical Engineering, Temple University, Philadelphia, PS 19122, United States |
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| Keywords | Electric taxi Cumulative prospect theory Trajectory data Charging decision-making Charging station Charging simulation algorithm |
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