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Location Selection of Charging Stations for Electric Taxis: A Bangkok Case

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  • Pichamon Keawthong

    (Technopreneurship and Innovation Management Program, Chulalongkorn University, Bangkok 10330, Thailand)

  • Veera Muangsin

    (Computer Engineering, Chulalongkorn University, Bangkok 10330, Thailand)

  • Chupun Gowanit

    (Technopreneurship and Innovation Management Program, Chulalongkorn University, Bangkok 10330, Thailand)

Abstract

The transition from ICE to BEV taxis is one of the most important methods for reducing fossil fuel consumption and air pollution in cities such as Bangkok. To support this transition, an adequate number of charging stations to cover each area of charging demand must be established. This paper presents a data-driven process for determining suitable charging locations for BEV taxis based on their characteristic driving patterns. The location selection process employs GPS trajectory data collected from taxis and the locations of candidate sites. Suitable locations are determined based on estimated travel times and charging demands. A queueing model is used to simulate charging activities and identify an appropriate number of chargers at each station. The location selection results are validated using data from existing charging services. The validation results show that the proposed process can recommend better locations for charging stations than current practices. By using the traveling time data that take the current traffic condition into account, e.g., via Google Maps API, we can minimize the overall travel time to charging stations of the taxi fleet better than using the distance data. This process can also be applied to other cities.

Suggested Citation

  • Pichamon Keawthong & Veera Muangsin & Chupun Gowanit, 2022. "Location Selection of Charging Stations for Electric Taxis: A Bangkok Case," Sustainability, MDPI, vol. 14(17), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:11033-:d:906279
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    References listed on IDEAS

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    1. Cilio, Luca & Babacan, Oytun, 2021. "Allocation optimisation of rapid charging stations in large urban areas to support fully electric taxi fleets," Applied Energy, Elsevier, vol. 295(C).
    2. Lin, Cheng-Chang & Lin, Chuan-Chih, 2018. "The p-center flow-refueling facility location problem," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 124-142.
    3. Wang, Hua & Zhao, De & Cai, Yutong & Meng, Qiang & Ong, Ghim Ping, 2021. "Taxi trajectory data based fast-charging facility planning for urban electric taxi systems," Applied Energy, Elsevier, vol. 286(C).
    4. Liu, Jin-peng & Zhang, Teng-xi & Zhu, Jiang & Ma, Tian-nan, 2018. "Allocation optimization of electric vehicle charging station (EVCS) considering with charging satisfaction and distributed renewables integration," Energy, Elsevier, vol. 164(C), pages 560-574.
    5. He, Sylvia Y. & Kuo, Yong-Hong & Sun, Ka Kit, 2022. "The spatial planning of public electric vehicle charging infrastructure in a high-density city using a contextualised location-allocation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 21-44.
    6. Zhu, Zhi-Hong & Gao, Zi-You & Zheng, Jian-Feng & Du, Hao-Ming, 2016. "Charging station location problem of plug-in electric vehicles," Journal of Transport Geography, Elsevier, vol. 52(C), pages 11-22.
    7. Li, Shengyin & Huang, Yongxi, 2014. "Heuristic approaches for the flow-based set covering problem with deviation paths," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 72(C), pages 144-158.
    8. Andrea Stabile & Michela Longo & Wahiba Yaïci & Federica Foiadelli, 2020. "An Algorithm for Optimization of Recharging Stops: A Case Study of Electric Vehicle Charging Stations on Canadian’s Ontario Highway 401," Energies, MDPI, vol. 13(8), pages 1-19, April.
    9. Rawan Shabbar & Anemone Kasasbeh & Mohamed M. Ahmed, 2021. "Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    10. Seoin Baek & Heetae Kim & Hyun Joon Chang, 2016. "A Feasibility Test on Adopting Electric Vehicles to Serve as Taxis in Daejeon Metropolitan City of South Korea," Sustainability, MDPI, vol. 8(9), pages 1-18, September.
    11. Erbaş, Mehmet & Kabak, Mehmet & Özceylan, Eren & Çetinkaya, Cihan, 2018. "Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis," Energy, Elsevier, vol. 163(C), pages 1017-1031.
    12. Andrenacci, N. & Ragona, R. & Valenti, G., 2016. "A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas," Applied Energy, Elsevier, vol. 182(C), pages 39-46.
    13. Sun, Zhuo & Gao, Wei & Li, Bin & Wang, Longlong, 2020. "Locating charging stations for electric vehicles," Transport Policy, Elsevier, vol. 98(C), pages 48-54.
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    Cited by:

    1. Jiusheng Du & Xingwang Liu & Chengyang Meng, 2023. "Road Intersection Extraction Based on Low-Frequency Vehicle Trajectory Data," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    2. Promporn Sornsoongnern & Suthatip Pueboobpaphan & Rattaphol Pueboobpaphan, 2023. "Innovative Dynamic Queue-Length Estimation Using Google Maps Color-Code Data," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    3. Wilfredo F. Yushimito & Sebastian Moreno & Daniela Miranda, 2023. "The Potential of Battery Electric Taxis in Santiago de Chile," Sustainability, MDPI, vol. 15(11), pages 1-15, May.

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