Exploring Bike-Sharing Behavior Affected by Public Transportation Disruption: Case of Washington, DC, Metro Shutdown
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 3
Abstract
The interaction between the bike-sharing behavior and public transportation system is still not fully understood. This paper aims to explore the impacts of public transportation disruption on the bike-sharing behavior utilizing the metro shutdown case in Washington, DC, in 2016. Multisource data, including bicycle trip and geographic information, was collected before, during, and after the public transportation disruption. Through the spatial-temporal analysis, it is a recurrent pattern for bike-sharing members to take their trips on weekdays and the casual users without membership to take the trips on weekends. Moreover, it is noted that the bike-sharing trips for casual user increased 191% and the variation of trip duration is insignificant on metro shutdown day. The bike-sharing stations adjacent to the metro lines are found to experience a higher trip increase. Furthermore, the complex network approach was employed to explore the bike-sharing network properties. In network perspective, the connectivity of bike-sharing network has increased due to the shifted travelers. In addition, the modularity maximization approach was also introduced to infer the community structure of bike-sharing network. The results suggest the disruption strengthen the interaction between central clusters and outer clusters. Implications for further research, policy, and design are discussed.
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Data Availability Statement
Data generated by the authors or analyzed during the study are available at the following. The 2016 bike-sharing dataset utilized to support the findings of this paper is derived from Capital Bikeshare, available at https://s3.amazonaws.com/capitalbikeshare-data/index.html. The geographic information is derived from Open data DC and is available at https://opendata.dc.gov.
Acknowledgments
The authors would like to thank the support from the National Natural Science Foundation of China (41901396) and Shandong Jianzhu University (X18052Z).
Disclaimer
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 3 • March 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Apr 20, 2020
Accepted: Oct 12, 2020
Published online: Dec 26, 2020
Published in print: Mar 1, 2021
Discussion open until: May 26, 2021
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