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Quantifying the impact of COVID-19 on non-motorized transportation: A Bayesian structural time series model

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  • Zhang, Yunchang
  • Fricker, Jon D.

Abstract

The coronavirus disease (COVID-19) pandemic has resulted in widespread impacts in the transportation sector due to containment measures. To better manage transportation during the COVID-19 crisis and improve future pre-pandemic planning, it is essential that we understand sufficiently the impact of the global epidemic on vehicle miles traveled, freight movement, and human mobility. The availability of pedestrian and bicycle count data allows us to estimate the causal impact of COVID-19 on non-motorized travel patterns. To quantify the causal effects of COVID-19, a Bayesian structural time series (BSTS) model is proposed, with the “treatment” date defined as the date on which the national emergency was declared. The model is intended to (1) account for variations in local trends, seasonality and exogeneous covariates before the treatment, (2) make predictions about the counterfactual trends after the treatment, (3) infer the causal effects between observed series and counterfactual series, and (4) evaluate the uncertainty about the causal inference.

Suggested Citation

  • Zhang, Yunchang & Fricker, Jon D., 2021. "Quantifying the impact of COVID-19 on non-motorized transportation: A Bayesian structural time series model," Transport Policy, Elsevier, vol. 103(C), pages 11-20.
    • Handle: RePEc:eee:trapol:v:103:y:2021:i:c:p:11-20
    DOI: 10.1016/j.tranpol.2021.01.013
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    7. Xingpei Yan & Zheng Zhu, 2021. "Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-15, August.
    8. Chen, Chao & Feng, Tao & Gu, Xiaoning & Yao, Baozhen, 2022. "Investigating the effectiveness of COVID-19 pandemic countermeasures on the use of public transport: A case study of The Netherlands," Transport Policy, Elsevier, vol. 117(C), pages 98-107.
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    10. Yang, Chao & Wan, Zhiyang & Yuan, Quan & Zhou, Yang & Sun, Maopeng, 2023. "Travel before, during and after the COVID-19 pandemic: Exploring factors in essential travel using empirical data," Journal of Transport Geography, Elsevier, vol. 110(C).
    11. Adibfar, Alireza & Gulhare, Siddhartha & Srinivasan, Siva & Costin, Aaron, 2022. "Analysis and modeling of changes in online shopping behavior due to Covid-19 pandemic: A Florida case study," Transport Policy, Elsevier, vol. 126(C), pages 162-176.
    12. Haiyan Liu & Jaeyoung Lee, 2023. "Contributing Factors to the Changes in Public and Private Transportation Mode Choice after the COVID-19 Outbreak in Urban Areas of China," Sustainability, MDPI, vol. 15(6), pages 1-21, March.
    13. Möllers, Alessa & Specht, Sebastian & Wessel, Jan, 2022. "The impact of the Covid-19 pandemic and government intervention on active mobility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 356-375.
    14. Shuai Yu & Bin Li & Dongmei Liu, 2023. "Exploring the Public Health of Travel Behaviors in High-Speed Railway Environment during the COVID-19 Pandemic from the Perspective of Trip Chain: A Case Study of Beijing–Tianjin–Hebei Urban Agglomera," IJERPH, MDPI, vol. 20(2), pages 1-22, January.
    15. Circella, Giovanni & Makino, Keita & Matson, Grant & Malik, Jai, 2022. "The Pulse of the Nation on 3 Revolutions: Annual Investigation of Nationwide Mobility Trends," Institute of Transportation Studies, Working Paper Series qt6h44p57d, Institute of Transportation Studies, UC Davis.
    16. Witold Torbacki, 2021. "Achieving Sustainable Mobility in the Szczecin Metropolitan Area in the Post-COVID-19 Era: The DEMATEL and PROMETHEE II Approach," Sustainability, MDPI, vol. 13(22), pages 1-25, November.

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