Quantifying the Traffic Impacts of the COVID-19 Shutdown
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 5
Abstract
The coronavirus disease (COVID-19) pandemic has significantly disrupted transportation and travel patterns across the US and around the world. A significant driving factor in the significant reduction in travel in the US was the declaration of varying state-, county-, and city-level stay-at-home orders with varying degrees of reduction. However, it is still not clear how significantly any one of those orders contributed to the reduction in travel. This article looks at continuous count data from the Minneapolis–St. Paul, Minnesota, area to quantify the disruption in terms of reductions in traffic volume as well as the abnormality of the disruption to travel patterns. A nearly 50% reduction in total traffic volume is found, and regional trends both in reductions and the gradual recovery toward normal travel patterns are identified. Furthermore, key dates are identified that led to significant reductions in travel, and this disruptive event is compared with other significantly disruptive events in Minnesota for context. It is found that although the stay-at-home order was a significant milestone in the fight against COVID-19, traffic volumes had already reduced significantly before the order went into effect, and traffic volumes had recovered significantly before the order expired. These findings will be helpful in understand the impact of stay-at-home orders on future outbreaks of COVID-19 or other pandemics.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This material is based upon work supported by the National Science Foundation under Grant No. CNS-2028946.
References
Abu-Rayash, A., and I. Dincer. 2020. “Analysis of mobility trends during the COVID-19 coronavirus pandemic: Exploring the impacts on global aviation and travel in selected cities.” Energy Res. Social Sci. 68 (Oct): 101693. https://doi.org/10.1016/j.erss.2020.101693.
Aletta, F., S. Brinchi, S. Carrese, A. Gemma, C. Guattari, L. Mannini, and S. M. Patella. 2020. “Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease.” Noise Mapp. 7 (1): 114–122. https://doi.org/10.1515/noise-2020-0010.
Athanesious, J. J., S. S. Chakkaravarthy, S. Vasuhi, and V. Vaidehi. 2019. “Trajectory based abnormal event detection in video traffic surveillance using general potential data field with spectral clustering.” Multimedia Tools Appl. 78 (14): 19877–19903. https://doi.org/10.1007/s11042-019-7332-y.
Beck, M. J., and D. A. Hensher. 2020. “Insights into the impact of COVID-19 on household travel and activities in Australia—The early days under restrictions.” Transp. Policy 96 (Sep): 76–93. https://doi.org/10.1016/j.tranpol.2020.07.001.
Chan, R., and J. L. Schofer. 2016. “Measuring transportation system resilience: Response of rail transit to weather disruptions.” Nat. Hazards Rev. 17 (1): 05015004. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000200.
Cherrett, T., B. Waterson, and M. McDonald. 2005. “Remote automatic incident detection using inductive loops.” In Vol. 158 of Proc., Institution of Civil Engineers-Transport, 149–155. London: Thomas Telford.
Dong, N., Z. Jia, J. Shao, Z. Xiong, Z. Li, F. Liu, J. Zhao, and P. Peng. 2010. “Traffic abnormality detection through directional motion behavior map.” In Proc., 7th IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, 80–84. New York: IEEE.
Donovan, B., and D. B. Work. 2017. “Empirically quantifying city-scale transportation system resilience to extreme events.” Transp. Res. Part C Emerging Technol. 79 (Jun): 333–346. https://doi.org/10.1016/j.trc.2017.03.002.
Glass, C. A., L. B. Davis, and X. Qu. 2018. “Visualizing the impact of severe weather disruptions to air transportation.” In Proc., 2018 IEEE Int. Conf. on Big Data, 3121–3127. New York: IEEE.
Gori, A., I. Gidaris, J. R. Elliott, J. Padgett, K. Loughran, P. Bedient, P. Panakkal, and A. Juan. 2020. “Accessibility and recovery assessment of Houston’s roadway network due to fluvial flooding during Hurricane Harvey.” Nat. Hazards Rev. 21 (2): 04020005. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000355.
Hendrickson, C., and L. R. Rilett. 2020. “The COVID-19 pandemic and transportation engineering.” J. Transp. Eng. Part A. Syst. 146 (7): 01820001. https://doi.org/10.1061/JTEPBS.0000418.
Lee, H., S. J. Park, G. R. Lee, J. E. Kim, J. H. Lee, Y. Jung, and E. W. Nam. 2020. “The relationship between the COVID-19 prevalence trend and transportation trend in South Korea.” Int. J. Infect. Dis. 93: 399–407. https://doi.org/10.1016/j.ijid.2020.05.031.
Li, Y., J. Wu, X. Bai, X. Yang, X. Tan, G. Li, S. Wen, H. Zhang, and E. Ding. 2020. “Multi-granularity tracking with modularlized components for unsupervised vehicles anomaly detection.” In Proc., IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops, 586–587. New York: IEEE.
Mahalanobis, P. C. 1936. On the generalized distance in statistics. Bengaluru, Karnataka, India: National Institute of Science of India.
Markolf, S. A., C. Hoehne, A. Fraser, M. V. Chester, and B. S. Underwood. 2019. “Transportation resilience to climate change and extreme weather events–beyond risk and robustness.” Transp. Policy 74 (Feb): 174–186. https://doi.org/10.1016/j.tranpol.2018.11.003.
Mercader, P., and J. Haddad. 2020. “Automatic incident detection on freeways based on Bluetooth traffic monitoring.” Accid. Anal. Prev. 146 (Oct): 105703. https://doi.org/10.1016/j.aap.2020.105703.
Parr, S., B. Wolshon, J. Renne, P. Murray-Tuite, and K. Kim. 2020. “Traffic impacts of the COVID-19 pandemic: Statewide analysis of social separation and activity restriction.” Nat. Hazards Rev. 21 (3): 04020025. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000409.
Pregnolato, M., A. Ford, V. Glenis, S. Wilkinson, and R. Dawson. 2017. “Impact of climate change on disruption to urban transport networks from pluvial flooding.” J. Infrastruct. Syst. 23 (4): 04017015. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000372.
Riveiro, M., M. Lebram, and M. Elmer. 2017. “Anomaly detection for road traffic: A visual analytics framework.” IEEE Trans. Intell. Transp. Syst. 18 (8): 2260–2270. https://doi.org/10.1109/TITS.2017.2675710.
Shen, G., and S. Aydin. 2014. “Highway freight transportation disruptions under an extreme environmental event: The case of Hurricane Katrina.” Int. J. Environ. Sci. Technol. 11 (8): 2387–2402. https://doi.org/10.1007/s13762-014-0677-x.
Xiang, J., E. Austin, T. Gould, T. Larson, J. Shirai, Y. Liu, J. Marshall, and E. Seto. 2020. “Impacts of the COVID-19 responses on traffic-related air pollution in a northwestern US city.” Sci. Total Environ. 747 (Dec): 141325. https://doi.org/10.1016/j.scitotenv.2020.141325.
Xu, B., H. Tian, C. E. Sabel, and B. Xu. 2019. “Impacts of road traffic network and socioeconomic factors on the diffusion of 2009 pandemic influenza a (H1N1) in mainland China.” Int. J. Environ. Res. Public Health 16 (7): 1223. https://doi.org/10.3390/ijerph16071223.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 5 • May 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Sep 22, 2020
Accepted: Jan 11, 2021
Published online: Feb 24, 2021
Published in print: May 1, 2021
Discussion open until: Jul 24, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.