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GPS data in taxi-sharing system: Analysis of potential demand and assessment of fuel consumption based on routing probability model

Author

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  • Yu, Qing
  • Li, Weifeng
  • Zhang, Haoran
  • Chen, Jinyu

Abstract

With the emergence of big geospatial data and the breakthrough of massive data processing, taxi-sharing offers the public a novel transportation mode with high comfort but low cost. However, designing a taxi-sharing system to effectively allocate taxi resources and provides high public acceptance services is an urgent problem to be solved. Furthermore, to what extent the taxi-sharing can be an eco-friendly service without bringing extra pressure to urban emission, fuel consumption, and transportation system is still an unanswered question. This paper proposes a methodology framework to design a taxi-sharing system with driver routing probability based matching and dispatching algorithms. The methodology is capable of matching multiple taxi trips into a sharing trip, with consideration of temporal and spatial feasibilities. The matching of sharing trips includes the determination of which trips to match and the sequence of the destinations. To examine the potential of operation efficiency improved and fuel consumption reduced in taxi-sharing, three scenarios are proposed with different constraints, representing different operation strategies. The sharing trips are then dispatched to the taxis. The potential of operating performance improvement and the potential of fuel consumption reduction are analyzed in the three scenarios. It is found that the delivery part of taxi-sharing may produce more travel distance because of the detour. The key factor for taxi-sharing service to reduce Vehicle Kilometres Travelled is the idle trips and the taxi resources saved. Considering both delivery trips and idle trips together, although taxi-sharing can reduce total fuel consumption in the city, it may increase traffic pressure in certain range area, especially in the key road sections or intersections in the urban road network and the area with high traffic demand.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s0306261922003452
    DOI: 10.1016/j.apenergy.2022.118923
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    References listed on IDEAS

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    1. 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).
    2. Yang, Chao & Du, Siyu & Li, Liang & You, Sixong & Yang, Yiyong & Zhao, Yue, 2017. "Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 203(C), pages 883-896.
    3. Wen-Long Shang & Yanyan Chen & Chengcheng Song & Washington Y. Ochieng, 2020. "Robustness Analysis of Urban Road Networks from Topological and Operational Perspectives," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, August.
    4. Yagcitekin, Bunyamin & Uzunoglu, Mehmet, 2016. "A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account," Applied Energy, Elsevier, vol. 167(C), pages 407-419.
    5. Sihai Zhang & Zhiyang Wang, 2016. "Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-21, November.
    6. Daniel J. Fagnant & Kara M. Kockelman, 2018. "Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas," Transportation, Springer, vol. 45(1), pages 143-158, January.
    7. Agatz, Niels A.H. & Erera, Alan L. & Savelsbergh, Martin W.P. & Wang, Xing, 2011. "Dynamic ride-sharing: A simulation study in metro Atlanta," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1450-1464.
    8. 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.
    9. Qian, Xinwu & Zhang, Wenbo & Ukkusuri, Satish V. & Yang, Chao, 2017. "Optimal assignment and incentive design in the taxi group ride problem," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 208-226.
    10. Shang, Wen-Long & Chen, Jinyu & Bi, Huibo & Sui, Yi & Chen, Yanyan & Yu, Haitao, 2021. "Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis," Applied Energy, Elsevier, vol. 285(C).
    11. Wen-Long Shang & Yanyan Chen & Huibo Bi & Haoran Zhang & Changxi Ma & Washington Y. Ochieng, 2020. "Statistical Characteristics and Community Analysis of Urban Road Networks," Complexity, Hindawi, vol. 2020, pages 1-21, September.
    12. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).
    13. Zhang, Haoran & Song, Xuan & Xia, Tianqi & Yuan, Meng & Fan, Zipei & Shibasaki, Ryosuke & Liang, Yongtu, 2018. "Battery electric vehicles in Japan: Human mobile behavior based adoption potential analysis and policy target response," Applied Energy, Elsevier, vol. 220(C), pages 527-535.
    14. Zhang, Haoran & Yan, Jinyue & Yu, Qing & Obersteiner, Michael & Li, Wenjing & Chen, Jinyu & Zhang, Qiong & Jiang, Mingkun & Wallin, Fredrik & Song, Xuan & Wu, Jiang & Wang, Xin & Shibasaki, Ryosuke, 2021. "1.6 Million transactions replicate distributed PV market slowdown by COVID-19 lockdown," Applied Energy, Elsevier, vol. 283(C).
    15. Zhang, Haoran & Song, Xuan & Long, Yin & Xia, Tianqi & Fang, Kai & Zheng, Jianqin & Huang, Dou & Shibasaki, Ryosuke & Liang, Yongtu, 2019. "Mobile phone GPS data in urban bicycle-sharing: Layout optimization and emissions reduction analysis," Applied Energy, Elsevier, vol. 242(C), pages 138-147.
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