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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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