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Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle

Author

Listed:
  • Bian, Zilin
  • Zuo, Fan
  • Gao, Jingqin
  • Chen, Yanyan
  • Pavuluri Venkata, Sai Sarath Chandra
  • Duran Bernardes, Suzana
  • Ozbay, Kaan
  • Ban, Xuegang (Jeff)
  • Wang, Jingxing

Abstract

The unprecedented challenges caused by the COVID-19 pandemic demand timely action. However, due to the complex nature of policy making, a lag may exist between the time a problem is recognized and the time a policy has its impact on a system. To understand this lag and to expedite decision making, this study proposes a change point detection framework using likelihood ratio, regression structure and a Bayesian change point detection method. The objective is to quantify the time lag effect reflected in transportation systems when authorities take action in response to the COVID-19 pandemic. Using travel patterns as an indicator of policy effectiveness, the length of policy lag and magnitude of policy impacts on the road system, mass transit, and micromobility are investigated through the case studies of New York City (NYC), and Seattle—two U.S. cities significantly affected by COVID-19. The quantitative findings show that the National declaration of emergency had no policy lag while stay-at-home and reopening policies had a lead effect on mobility. The magnitude of impact largely depended on the land use and sociodemographic characteristics of the area, as well as the type of transportation system.

Suggested Citation

  • Bian, Zilin & Zuo, Fan & Gao, Jingqin & Chen, Yanyan & Pavuluri Venkata, Sai Sarath Chandra & Duran Bernardes, Suzana & Ozbay, Kaan & Ban, Xuegang (Jeff) & Wang, Jingxing, 2021. "Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 269-283.
    • Handle: RePEc:eee:transa:v:145:y:2021:i:c:p:269-283
    DOI: 10.1016/j.tra.2021.01.019
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    14. Ma, Changxi & Zhang, Bowen & Li, Shukai & Lu, Youpeng, 2024. "Urban rail transit passenger flow prediction with ResCNN-GRU based on self-attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
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