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

Author

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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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    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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