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Citation |
Huo Z, Yang X, Liu X, Yan X. Transp. Res. A Policy Pract. 2024; 183: e104047. |
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Copyright |
(Copyright © 2024, Elsevier Publishing) |
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DOI |
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PMID |
unavailable |
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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. |
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Keywords |
Cluster-based approach; Geographically weighted regression; Land use; Nonnegative matrix factorization; Online designated driving; Travel patterns |