TY - JOUR
T1 - Time lag effects of COVID-19 policies on transportation systems
T2 - A comparative study of New York City and Seattle
AU - Bian, Zilin
AU - Zuo, Fan
AU - Gao, Jingqin
AU - Chen, Yanyan
AU - Pavuluri Venkata, Sai Sarath Chandra
AU - Duran Bernardes, Suzana
AU - Ozbay, Kaan
AU - Ban, Xuegang (Jeff)
AU - Wang, Jingxing
N1 - Funding Information:
This work was supported by the Connected Cities for Smart Mobility towards Accessible and Resilient Transportation (C2SMART) Center, a Tier 1 University Center awarded by U.S. Department of Transportation under the University Transportation Centers Program. The authors acknowledge TRANSIT APP for sharing transit demand data. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This work is funded, partially or entirely, by a grant from the U.S. Department of Transportation’s University Transportation Centers Program. However, the U.S. Government assumes no liability for the contents or use thereof.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
KW - COVID-19
KW - Change point detection
KW - Lessons learned
KW - Policy lag
KW - Time effect
UR - http://www.scopus.com/inward/record.url?scp=85100436940&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100436940&partnerID=8YFLogxK
U2 - 10.1016/j.tra.2021.01.019
DO - 10.1016/j.tra.2021.01.019
M3 - Article
AN - SCOPUS:85100436940
SN - 0965-8564
VL - 145
SP - 269
EP - 283
JO - Transportation Research, Part A: Policy and Practice
JF - Transportation Research, Part A: Policy and Practice
ER -