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Identifying the collaborative scheduling areas between ride-hailing and traditional taxi services based on vehicle trajectory data

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  • Zhao, Zhiyuan
  • Yao, Wei
  • Wu, Sheng
  • Yang, Xiping
  • Wu, Qunyong
  • Fang, Zhixiang

Abstract

Traditional taxi services (TTSs) play an important role in satisfying daily travel demands. The rapid growth of ride-hailing services (RHSs) has increased the convenience of customized travel. However, the volume of empty ride-hailing vehicles has increased. Identifying the collaborative scheduling areas (CoSAs) between RHSs and TTSs can further improve the efficiency of urban travel services. Therefore, we propose a method to identify CoSAs based on the travel demand and vehicle supply of TTSs and RHSs derived from trajectory data. We first optimize and make the temporal resolution of the trajectories of different types of vehicles uniform based on the shortest path algorithm. Then, the indicators describing travel demand and vehicle supply are defined and calculated. Finally, the areas with a high vehicle supply of one type and a low vehicle supply and high travel demand of the other type are identified as CoSAs. A dataset for Xiamen Island indicates that the CoSAs of TTSs to RHSs can provide potential routes to pick up passengers that are 41% and 11% shorter than the actual routes at 9:00 and 18:00, respectively. The constructed method can also improve the CoSA identification results.

Suggested Citation

  • Zhao, Zhiyuan & Yao, Wei & Wu, Sheng & Yang, Xiping & Wu, Qunyong & Fang, Zhixiang, 2023. "Identifying the collaborative scheduling areas between ride-hailing and traditional taxi services based on vehicle trajectory data," Journal of Transport Geography, Elsevier, vol. 107(C).
    • Handle: RePEc:eee:jotrge:v:107:y:2023:i:c:s0966692323000169
    DOI: 10.1016/j.jtrangeo.2023.103544
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    1. Erdem Ozdemir & Ahmet E. Topcu & Mehmet Kemal Ozdemir, 2018. "A hybrid HMM model for travel path inference with sparse GPS samples," Transportation, Springer, vol. 45(1), pages 233-246, January.
    2. Roberto Ponce Lopez & Joseph Ferreira, 2021. "Identifying spatio-temporal hotspots of human activity that are popular non-work destinations," Environment and Planning B, , vol. 48(3), pages 433-448, March.
    3. Tang, Jinjun & Zhang, Shen & Zhang, Wenhui & Liu, Fang & Zhang, Weibin & Wang, Yinhai, 2016. "Statistical properties of urban mobility from location-based travel networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 694-707.
    4. Zhong, Yuanguang & Yang, Tong & Cao, Bin & Cheng, T.C.E., 2022. "On-demand ride-hailing platforms in competition with the taxi industry: Pricing strategies and government supervision," International Journal of Production Economics, Elsevier, vol. 243(C).
    5. Chen, Fangxi & Yin, Zhiwei & Ye, Yingwei & Sun, Daniel(Jian), 2020. "Taxi hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data," Transport Policy, Elsevier, vol. 97(C), pages 73-84.
    6. Joshua D. Angrist & Sydnee Caldwell & Jonathan V. Hall, 2021. "Uber versus Taxi: A Driver's Eye View," American Economic Journal: Applied Economics, American Economic Association, vol. 13(3), pages 272-308, July.
    7. Zhang, Bin & Chen, Shuyan & Ma, Yongfeng & Li, Tiezhu & Tang, Kun, 2020. "Analysis on spatiotemporal urban mobility based on online car-hailing data," Journal of Transport Geography, Elsevier, vol. 82(C).
    8. Dong, Xianlei & Zhang, Min & Zhang, Shuang & Shen, Xinyi & Hu, Beibei, 2019. "The analysis of urban taxi operation efficiency based on GPS trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C).
    9. Fangru Wang & Catherine L. Ross, 2019. "New potential for multimodal connection: exploring the relationship between taxi and transit in New York City (NYC)," Transportation, Springer, vol. 46(3), pages 1051-1072, June.
    10. Xiao, Chaowei & Silva, Elisabete A. & Zhang, Chuchu, 2020. "Nine-nine-six work system and people’s movement patterns: Using big data sets to analyse overtime working in Shanghai," Land Use Policy, Elsevier, vol. 90(C).
    11. Judd Cramer & Alan B. Krueger, 2016. "Disruptive Change in the Taxi Business: The Case of Uber," American Economic Review, American Economic Association, vol. 106(5), pages 177-182, May.
    12. Li, Xijing & Ma, Xinlin & Wilson, Bev, 2021. "Beyond absolute space: An exploration of relative and relational space in Shanghai using taxi trajectory data," Journal of Transport Geography, Elsevier, vol. 93(C).
    13. Mingyang Du & Lin Cheng & Xuefeng Li & Jingzong Yang, 2019. "Investigating the Influential Factors of Shared Travel Behavior: Comparison between App-Based Third Taxi Service and Free-Floating Bike Sharing in Nanjing, China," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    14. Zhang, Xiaohu & Xu, Yang & Tu, Wei & Ratti, Carlo, 2018. "Do different datasets tell the same story about urban mobility — A comparative study of public transit and taxi usage," Journal of Transport Geography, Elsevier, vol. 70(C), pages 78-90.
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