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Environmental benefits of taxi ride sharing in Beijing

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  • Cai, Hua
  • Wang, Xi
  • Adriaens, Peter
  • Xu, Ming

Abstract

Although ride sharing as a way to improve transportation efficiency is not new, the scale of ride sharing has historically been limited due to safety concerns and logistics challenges. Recent developments in information and communications technology (ICT) enable real-time sharing of individual geographical information, allow for easier participation in the “sharing economy”, and present opportunities for implementing ride sharing at a large-scale. This research aims to quantify the environmental benefits of ride sharing using shared taxis in Beijing as a case study. Trip information extracted from vehicle trajectory data of 12,083 taxis in Beijing are used to evaluate the benefits of ride sharing. Our results show that: 1) only limited opportunity cost to the rider (i.e. tolerance of early departure or delayed arrival) can enable large-scale ride sharing; 2) at a tolerance level of 10 min, ride sharing can reduce fleet vehicle-miles-traveled (VMT) by 33%; and 3) if implemented for the entire taxi fleet, shared taxis can save 28.3 million gallons of gasoline and reduce 186 tons VOC, 199 tons NOx, 53 tons PM10, 25 tons PM2.5, and 2392 tons CO emissions annually.

Suggested Citation

  • Cai, Hua & Wang, Xi & Adriaens, Peter & Xu, Ming, 2019. "Environmental benefits of taxi ride sharing in Beijing," Energy, Elsevier, vol. 174(C), pages 503-508.
    • Handle: RePEc:eee:energy:v:174:y:2019:i:c:p:503-508
    DOI: 10.1016/j.energy.2019.02.166
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    Cited by:

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    2. Feng, Xuan & Lin, Qinping & Jia, Ning & Tian, Junfang, 2024. "The actual impact of ride-splitting: An empirical study based on large-scale GPS data," Transport Policy, Elsevier, vol. 147(C), pages 94-112.
    3. Yu, Qing & Li, Weifeng & Zhang, Haoran & Chen, Jinyu, 2022. "GPS data in taxi-sharing system: Analysis of potential demand and assessment of fuel consumption based on routing probability model," Applied Energy, Elsevier, vol. 314(C).
    4. Haoran Chen & Xuedong Yan & Xiaobing Liu & Tao Ma, 2023. "Exploring the operational performance discrepancies between online ridesplitting and carpooling transportation modes based on DiDi data," Transportation, Springer, vol. 50(5), pages 1923-1958, October.
    5. Chiappetta Jabbour, Charbel Jose & De Camargo Fiorini, Paula & Wong, Christina W.Y. & Jugend, Daniel & Lopes De Sousa Jabbour, Ana Beatriz & Roman Pais Seles, Bruno Michel & Paula Pinheiro, Marco Anto, 2020. "First-mover firms in the transition towards the sharing economy in metallic natural resource-intensive industries: Implications for the circular economy and emerging industry 4.0 technologies," Resources Policy, Elsevier, vol. 66(C).
    6. Yanhong Yin & Han Wang & Jimin Xiong & Yufeng Zhu & Zhanfeng Tang, 2021. "Estimation of optimum supply of shared cars based on personal travel behaviors in condition of minimum energy consumption," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13324-13339, September.

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