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Bikeshare and subway ridership changes during the COVID-19 pandemic in New York City

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  • Wang, Haoyun
  • Noland, Robert B.

Abstract

The COVID-19 pandemic has been unprecedented in its scale and speed, impacting the entire world, and having an impact on metropolitan transportation systems. New York City (NYC) was especially hard hit in March and April 2020. A mandatory stay-at-home order was instituted, with all but essential businesses ordered closed. In this paper we examine the impact on the Citi Bike system and the NYC subway. Usage patterns during the lockdown are compared to corresponding days in 2019. Controlling for weather patterns we examine the effect of the lockdown and subsequent reopening of economic activity up through the end of September 2020. The results show that both subway ridership and bikeshare usage plummeted initially; bikeshare usage has nearly returned to normal while subway ridership remains substantially below pre-COVID levels. Implications for policy suggest that the bikeshare system provides resilience to the overall transportation system during disasters when public transit is considered dangerous or is disrupted.

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  • Wang, Haoyun & Noland, Robert B., 2021. "Bikeshare and subway ridership changes during the COVID-19 pandemic in New York City," Transport Policy, Elsevier, vol. 106(C), pages 262-270.
    • Handle: RePEc:eee:trapol:v:106:y:2021:i:c:p:262-270
    DOI: 10.1016/j.tranpol.2021.04.004
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    References listed on IDEAS

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    3. Dehdari Ebrahimi, Zhila & Momenitabar, Mohsen & Nasri, Arefeh A. & Mattson, Jeremy, 2022. "Using a GIS-based spatial approach to determine the optimal locations of bikeshare stations: The case of Washington D.C," Transport Policy, Elsevier, vol. 127(C), pages 48-60.
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    7. Zhang, Xiang & Li, Wence, 2023. "Effects of a bike sharing system and COVID-19 on low-carbon traffic modal shift and emission reduction," Transport Policy, Elsevier, vol. 132(C), pages 42-64.
    8. Elif Bozkaya & Levent Eriskin & Mumtaz Karatas, 2023. "Data analytics during pandemics: a transportation and location planning perspective," Annals of Operations Research, Springer, vol. 328(1), pages 193-244, September.
    9. Fan, Huiying & Lu, Hongyu & Guin, Angshuman & Watkins, Kari E & Guensler, Randall, 2022. "Combined Effect of Changes in Transit Service and Changes in Occupancy on Per-Passenger Energy Consumption," Institute of Transportation Studies, Working Paper Series qt2x1320p5, Institute of Transportation Studies, UC Davis.
    10. Ali El Zein & Adrien Beziat & Pascal Pochet & Olivier Klein & Stephanie Vincent, 2022. "What drives the changes in public transport use in the context of the COVID‐19 pandemic? Highlights from Lyon metropolitan area," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(S1), pages 122-141, November.
    11. Xin, Mengwei & Shalaby, Amer & Feng, Shumin & Zhao, Hu, 2021. "Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method," Transport Policy, Elsevier, vol. 111(C), pages 1-16.
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    13. Xinyi Xie & Mingyang Du & Xuefeng Li & Yunjian Jiang, 2023. "Exploring Influential Factors of Free-Floating Bike-Sharing Usage Frequency before and after COVID-19," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    14. Jiang, Shixiong & Cai, Canhuang, 2022. "Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China," Transport Policy, Elsevier, vol. 127(C), pages 158-170.
    15. Liang, Zhiyuan & Tang, Yili & Yu, Jianing & Wang, Yacan, 2024. "A collective incentive strategy to manage ridership rebound and consumer surplus in mass transit systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    16. Ziedan, Abubakr & Lima, Luiz & Brakewood, Candace, 2023. "A multiple mediation analysis to untangle the impacts of COVID-19 on nationwide bus ridership in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    17. Filipe Teixeira, João & Silva, Cecília & Moura e Sá, Frederico, 2022. "The role of bike sharing during the coronavirus pandemic: An analysis of the mobility patterns and perceptions of Lisbon’s GIRA users," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 17-34.
    18. Yi Cao & Xue Li, 2022. "Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19," Sustainability, MDPI, vol. 14(21), pages 1-16, November.

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