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Spatio-temporal analysis on online designated driving based on empirical data

Author

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  • HUO, Zhengqi
  • YANG, Xiaobao
  • LIU, Xiaobing
  • YAN, Xuedong

Abstract

In recent years, mobility-on-demand services have experienced rapid growth and become an integral part of the urban transport system. As one of the prevalent mobility-on-demand services, online designated driving allows car-owners booking professional drivers via mobile apps to deliver private cars (frequently with the car-owners inside) to a designated location for a fee. Online designated driving can play a crucial role in promoting road safety by providing a responsible alternative for those who cannot drive themselves due to drinking or fatigue, but there is still a lack of empirical study on its basic travel patterns. Using massive trip records of online designated driving, this study investigates the collective and individual spatio-temporal travel patterns, and reveals the spatio-temporal impact mechanism of various land-use types on online designated driving demand. Take Beijing as a case study, the results show that unlike ride-hailing trips that primarily occur during the daytime, 93.07 % of online designated driving trips occurs between 18:00 and 7:00 the next day, with the longest peak observed on Friday nights. Meanwhile, the average distance of online designated driving trips is approximately twice as long as that of ride-hailing trips (16.58 km vs. 8.48 km). The collective travel patterns of online designated driving exhibit a shrinking trend in the form of “surface-line-point” over time. Regarding individual patterns, we rigorously demonstrate that 81.55 % of high-frequency users exhibit a “fixed-destination” phenomenon, indicating a clear centripetal tendency. Furthermore, online designated driving is primarily associated with limited land-use types, revealing that it may serve specific target audiences, like travelers returning from entertainment and catering activities after consuming alcohol, or those in need of transportation for business and automotive services. These findings can contribute to refining operational practices and enhancing regulatory measures for online designated driving.

Suggested Citation

  • HUO, Zhengqi & YANG, Xiaobao & LIU, Xiaobing & YAN, Xuedong, 2024. "Spatio-temporal analysis on online designated driving based on empirical data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:transa:v:183:y:2024:i:c:s0965856424000958
    DOI: 10.1016/j.tra.2024.104047
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    1. Yang, Hongtai & Luo, Peng & Li, Chaojing & Zhai, Guocong & Yeh, Anthony G.O., 2023. "Nonlinear effects of fare discounts and built environment on ridesplitting adoption rates," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    2. Sanaullah, Irum & Alsaleh, Nael & Djavadian, Shadi & Farooq, Bilal, 2021. "Spatio-temporal analysis of on-demand transit: A case study of Belleville, Canada," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 284-301.
    3. 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).
    4. Guanwei Zhao & Zhitao Li & Yuzhen Shang & Muzhuang Yang, 2022. "How Does the Urban Built Environment Affect Online Car-Hailing Ridership Intensity among Different Scales?," IJERPH, MDPI, vol. 19(9), pages 1-25, April.
    5. Aghaabbasi, Mahdi & Shekari, Zohreh Asadi & Shah, Muhammad Zaly & Olakunle, Oloruntobi & Armaghani, Danial Jahed & Moeinaddini, Mehdi, 2020. "Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 262-281.
    6. Du, Mingyang & Cheng, Lin & Li, Xuefeng & Liu, Qiyang & Yang, Jingzong, 2022. "Spatial variation of ridesplitting adoption rate in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 13-37.
    7. Pan, Yingjiu & Chen, Shuyan & Niu, Shifeng & Ma, Yongfeng & Tang, Kun, 2020. "Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity," Journal of Transport Geography, Elsevier, vol. 83(C).
    8. Rong-Chang Jou & Li-Wun Syu, 2021. "Drunk Drivers’ Willingness to Use and to Pay for Designated Drivers," Sustainability, MDPI, vol. 13(10), pages 1-23, May.
    9. Ma, Xinwei & Ji, Yanjie & Yuan, Yufei & Van Oort, Niels & Jin, Yuchuan & Hoogendoorn, Serge, 2020. "A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 148-173.
    10. Yu, Haitao & Peng, Zhong-Ren, 2019. "Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression," Journal of Transport Geography, Elsevier, vol. 75(C), pages 147-163.
    11. Abouelela, Mohamed & Chaniotakis, Emmanouil & Antoniou, Constantinos, 2023. "Understanding the landscape of shared-e-scooters in North America; Spatiotemporal analysis and policy insights," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    12. He, Zhengbing, 2021. "Portraying ride-hailing mobility using multi-day trip order data: A case study of Beijing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 152-169.
    13. Shaopeng Zhong & Daniel (Jian) Sun, 2022. "Logic-Driven Traffic Big Data Analytics: An Introduction," Springer Books, in: Logic-Driven Traffic Big Data Analytics, chapter 0, pages 1-32, Springer.
    14. Chen, Chao & Feng, Tao & Ding, Chuan & Yu, Bin & Yao, Baozhen, 2021. "Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model," Journal of Transport Geography, Elsevier, vol. 96(C).
    15. Chengbin Peng & Xiaogang Jin & Ka-Chun Wong & Meixia Shi & Pietro Liò, 2012. "Collective Human Mobility Pattern from Taxi Trips in Urban Area," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-8, April.
    16. 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.
    17. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
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