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A trip-specific model for fuel saving estimation and subsidy policy making of carpooling based on empirical data

Author

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  • Liu, Xiaobing
  • Yan, Xuedong
  • Liu, Feng
  • Wang, Rui
  • Leng, Yan

Abstract

As an eco-friendly and convenient transportation mode, mobile internet-based carpooling has achieved mushroom growth in many cities in recent years. Theoretical studies have verified that ridesharing is not only beneficial to drivers and passengers but particularly to the environment. Nevertheless, the exact impact of ridesharing on energy consumption and exhaust emission has been barely explored based on real carpooling data. In this study, using massive mobile internet based carpooling data offered by DiDi Company, a trip-specific model was initially proposed to study the intrinsic mechanism of carpooling services and then estimate the fuel savings of individual carpooling trip. According to the estimation results, delicacy subsidy strategies under the Personal Carbon Trading scheme were suggested to guarantee the moderation and equity in promoting carpooling services. The developed methodology was further tested in the case city of Beijing and associated results showed that ridesharing could be a feeder for public transit to support the commuting demands of workers living in suburban. More importantly, the fuel savings of ridesharing are considerable, every trip saving 1.23 L on average, and the carbon subsidies are moderate, per trip reaching ¥5.38 with the strictest subsidy ceiling. From the spatial-temporal perspective, the Chaoyang district and the daily peak-hour period generate the largest number of both ridesharing orders and fuel savings. All the results demonstrate that the trip-specific model has the advantages of delicacy, reliability and accuracy, which could facilitate the estimation on the trip-specific fuel savings and the formulation of carpooling promotion strategies.

Suggested Citation

  • Liu, Xiaobing & Yan, Xuedong & Liu, Feng & Wang, Rui & Leng, Yan, 2019. "A trip-specific model for fuel saving estimation and subsidy policy making of carpooling based on empirical data," Applied Energy, Elsevier, vol. 240(C), pages 295-311.
    • Handle: RePEc:eee:appene:v:240:y:2019:i:c:p:295-311
    DOI: 10.1016/j.apenergy.2019.02.003
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    3. Chen, Long & Huang, Jiahui & Jing, Peng & Wang, Bichen & Yu, Xiaozhou & Zha, Ye & Jiang, Chengxi, 2023. "Changing or unchanging Chinese attitudes toward ride-hailing? A social media analytics perspective from 2018 to 2021," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    4. Anne Aguiléra & Eléonore Pigalle, 2021. "The Future and Sustainability of Carpooling Practices. An Identification of Research Challenges," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    5. Michel Noussan & Matteo Jarre, 2021. "Assessing Commuting Energy and Emissions Savings through Remote Working and Carpooling: Lessons from an Italian Region," Energies, MDPI, vol. 14(21), pages 1-19, November.
    6. Wu, Tian & Wang, Shouyang & Wang, Lining & Tang, Xiao, 2022. "Contribution of China's online car-hailing services to its 2050 carbon target: Energy consumption assessment based on the GCAM-SE model," Energy Policy, Elsevier, vol. 160(C).
    7. Leonidas G. Anthopoulos & Dimitrios N. Tzimos, 2021. "Carpooling Platforms as Smart City Projects: A Bibliometric Analysis and Systematic Literature Review," Sustainability, MDPI, vol. 13(19), pages 1-29, September.
    8. 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.
    9. Wenyuan Zhou & Xuanrong Li & Zhenguo Shi & Bingjie Yang & Dongxu Chen, 2023. "Impact of Carpooling under Mobile Internet on Travel Mode Choices and Urban Traffic Volume: The Case of China," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    10. 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.
    11. Zhang, Haoran & Chen, Jinyu & Li, Wenjing & Song, Xuan & Shibasaki, Ryosuke, 2020. "Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential," Applied Energy, Elsevier, vol. 269(C).
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    13. María del Carmen Rey-Merchán & Antonio López-Arquillos & Manuela Pires Rosa, 2022. "Carpooling Systems for Commuting among Teachers: An Expert Panel Analysis of Their Barriers and Incentives," IJERPH, MDPI, vol. 19(14), pages 1-12, July.
    14. Lei Wang & Wenxiang Li & Jinxian Weng & Dong Zhang & Wanjing Ma, 2023. "Do low-carbon rewards incentivize people to ridesplitting? Evidence from structural analysis," Transportation, Springer, vol. 50(5), pages 2077-2109, October.

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