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Siting of Carsharing Stations Based on Spatial Multi-Criteria Evaluation: A Case Study of Shanghai EVCARD

Author

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  • Wenxiang Li

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
    College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Ye Li

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
    College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Jing Fan

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
    College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Haopeng Deng

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
    College of Transportation Engineering, Tongji University, Shanghai 201804, China)

Abstract

Carsharing is one of the effective ways to relieve the problems of traffic jams, parking difficulties, and air pollution. In recent years, the numbers of carsharing services and their members have remarkably increased around the world. The project of electric carsharing in Shanghai, called EVCARD, has also developed rapidly with very large demand and supply. Aiming to determine the optimal locations of future stations of the EVCARD, this research employs a novel method combining the analytic hierarchy process (AHP) and geographical information system (GIS) with big data. Potential users, potential travel demand, potential travel purposes, and distance from existing stations are selected as the decision criteria. A siting decision system is established, consisting of 15 evaluation indicators which are calculated from multi-source data on mobile phones, taxi trajectory, point of interests (POI), and the EVCARD operation. The method of the AHP is used to determine the indicator weights, and the “Spatial Analyst” tool of ArcGIS is adopted to generate the indicator values for every 1 km × 1 km decision unit. Finally, synthetic scores are calculated to evaluate the candidate sites of EVCARD stations. The results of the case study verify the effectiveness of the proposed method, which can provide a more scientific and feasible method for carsharing operators to site stations, avoiding aimless and random decisions.

Suggested Citation

  • Wenxiang Li & Ye Li & Jing Fan & Haopeng Deng, 2017. "Siting of Carsharing Stations Based on Spatial Multi-Criteria Evaluation: A Case Study of Shanghai EVCARD," Sustainability, MDPI, vol. 9(1), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:1:p:152-:d:88348
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    5. Zou, Guojian & Lai, Ziliang & Li, Ye & Liu, Xinghua & Li, Wenxiang, 2022. "Exploring the nonlinear impact of air pollution on housing prices: A machine learning approach," Economics of Transportation, Elsevier, vol. 31(C).
    6. Jarosław Wątróbski & Krzysztof Małecki & Kinga Kijewska & Stanisław Iwan & Artur Karczmarczyk & Russell G. Thompson, 2017. "Multi-Criteria Analysis of Electric Vans for City Logistics," Sustainability, MDPI, vol. 9(8), pages 1-34, August.
    7. Szabolcs Duleba & Sarbast Moslem, 2018. "Sustainable Urban Transport Development with Stakeholder Participation, an AHP-Kendall Model: A Case Study for Mersin," Sustainability, MDPI, vol. 10(10), pages 1-14, October.
    8. Katarzyna Turoń & Andrzej Kubik & Feng Chen, 2022. "What Car for Car-Sharing? Conventional, Electric, Hybrid or Hydrogen Fleet? Analysis of the Vehicle Selection Criteria for Car-Sharing Systems," Energies, MDPI, vol. 15(12), pages 1-14, June.
    9. Vanduy Tran & Shengchuan Zhao & El Bachir Diop & Weiya Song, 2019. "Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China," Sustainability, MDPI, vol. 11(19), pages 1-22, September.
    10. Feng, Xiaoyan & Sun, Huijun & Wu, Jianjun & Liu, Zhiyuan & Lv, Ying, 2020. "Trip chain based usage patterns analysis of the round-trip carsharing system: A case study in Beijing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 190-203.
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