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2021-10-25
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Copyright (c) 2021 Tao Tao, Jason Cao
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How to Cite
Exploring the interaction effect of poverty concentration and transit service on highway traffic during the COVID-19 lockdown
Tao Tao
Jason Cao
DOI: https://doi.org/10.5198/jtlu.2021.1978
Keywords: COVID-19, stay-at-home order, social equity, transit service, transportation, low-income people
Abstract
During COVID-19 lockdowns, transit agencies need to respond to the decline in travel but also maintain the essential mobility of transit-dependent people. However, there are a few lessons that scholars and practitioners can learn from. Using highway traffic data in the Twin Cities, this study applies a generalized additive model to explore the relationships among the share of low-income population, transit service, and highway traffic during the week that occurred right after the 2020 stay-at-home order. Our results substantiate that transportation impacts are spread unevenly across different income groups and low-income people are less able to reduce travel, leading to equity concerns. Moreover, transit supply influences highway traffic differently in areas with different shares of low-income people. Our study suggests that transportation agencies should provide more affordable travel options for areas with concentrated poverty during lockdowns. In addition, transit agencies should manage transit supply strategically depending on the share of low-income people to better meet people’s mobility needs.
References
APTA. (2020). Public transportation ridership report. APTA. Retrieved July 28, 2020, from https://www.apta.com/research-technical-resources/transit-statistics/ridership-report/
Asmus, A., & Ehrlich, J. (2020). Changes to metro-area travel during the COVID-19 (coronavirus) outbreak. Center for Transportation Studies, University of Minnesota. Retrieved July 28, 2020, from http://www.cts.umn.edu/sites/default/files/files/events/Seminars/2020webinar/Ehrlich_webinar.pdf
Beck, M. J., & Hensher, D. A. (2020). Insights into the impact of COVID-19 on household travel and activities in Australia – The early days under restrictions. Transport Policy, 96(May), 76–93. https://doi.org/10.1016/j.tranpol.2020.07.001
Blumenberg, E. (2017). Social equity and urban transporation. In G. Giuliano & S. Hanson (Eds.), The geography of urban transportation (4th ed.). New York: The Guilford Press. https://ebookcentral.proquest.com/lib/umn/detail.action?docID=4832774
Brough, R., Freedman, M., & Phillips, D. (2020). Understanding socioeconomic disparities in travel behavior during the COVID-19 pandemic. SSRN Electronic Journal, (June). https://doi.org/10.2139/ssrn.3624920
Bucsky, P. (2020). Modal share changes due to COVID-19: The case of Budapest. Transportation Research Interdisciplinary Perspectives, 8 100141. https://doi.org/10.1016/j.trip.2020.100141
Cao, X. J., Xu, Z., & Douma, F. (2012). The interactions between e-shopping and traditional in-store shopping: An application of structural equations model. Transportation, 39(5), 957–974. https://doi.org/10.1007/s11116-011-9376-3
Cole, B. (2020). Pennsylvania food bank draws mile-long line of cars as Trump approves state’s coronavirus disaster declaration. Newsweek. Retrieved from https://www.newsweek.com/pennsylvania-food-bank-draws-mile-long-line-cars-trump-approves-states-coronavirus-disaster-1495222
de Haas, M., Faber, R., & Hamersma, M. (2020). How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behavior: Evidence from longitudinal data in the Netherlands. Transportation Research Interdisciplinary Perspectives, 6, 100150. https://doi.org/10.1016/j.trip.2020.100150
De Vos, J. (2020). The effect of COVID-19 and subsequent social distancing on travel behavior. Transportation Research Interdisciplinary Perspectives, 5, 100121. https://doi.org/10.1016/j.trip.2020.100121
FHWA. (2020). April 2020 traffic volume trends. FHWA. Retrieved July 28, 2020, from https://www.fhwa.dot.gov/policyinformation/travel_monitoring/20aprtvt/
Hu, S., & Chen, P. (2021). Who left riding transit? Examining socioeconomic disparities in the impact of COVID-19 on ridership. Transportation Research Part D: Transport and Environment, 90, 102654. https://doi.org/10.1016/j.trd.2020.102654
Hu, S., Chen, P., Lin, H., Xie, C., & Chen, X. (2018). Promoting carsharing attractiveness and efficiency: An exploratory analysis. Transportation Research Part D: Transport and Environment, 65, 229–243. https://doi.org/10.1016/j.trd.2018.08.015
Kerkman, K., Martens, K., & Meurs, H. (2018). Predicting travel flows with spatially explicit aggregate models: On the benefits of including spatial dependence in travel demand modeling. Transportation Research Part A: Policy and Practice, 118(October 2017), 68–88. https://doi.org/10.1016/j.tra.2018.08.029
Lin, X., & Zhang, D. (1999). Inference in generalized additive mixed models by using smoothing splines. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 61(2), 381–400. https://doi.org/10.1111/1467-9868.00183
Lind, E. (2020). Understanding the essential transit market: Transit service & ridership during COVID-19. Center for Transportation Studies, University of Minnesota. Retrieved July 28, 2020, from http://www.cts.umn.edu/sites/default/files/files/events/Seminars/2020webinar/Lind_webinar.pdf
Met Council & Metropolitan Council. (2020). COVID-19 outbreak – Metro area travel declines. Retrieved July 24, 2020, from https://metrotransitmn.shinyapps.io/covid-traffic-trends/
Metro Transit. (2020, July 22). Metro Transit’s response to the coronavirus (COVID-19). Retrieved from https://www.metrotransit.org/health
Parr, S., Wolshon, B., Renne, J., Murray-Tuite, P., & Kim, K. (2020). Traffic impacts of the COVID-19 pandemic: Statewide analysis of social separation and activity restriction. Natural Hazards Review, 21(3), 04020025. https://doi.org/10.1061/(asce)nh.1527-6996.0000409
Redman, R. (2020). Nearly 80% of Americans shopped online for groceries during crisis. Supermarket News. Retrieved July 28, 2020, from https://www.supermarketnews.com/online-retail/nearly-80-us-consumers-shopped-online-groceries-covid-19-outbreak
Renne, J. L., & Bennett, P. (2014). Socioeconomics of urban travel: Evidence from the 2009 National Household Travel Survey with implications for sustainability. World Transport Policy & Practice, 20(4), 7–27.
Rho, H. J., Brown, H., & Fremstad, S. (2020). A basic demographic profile of workers in frontline industries. Washington, DC: Center for Economic and Policy Research. Retrieved from https://cepr.net/wp-content/uploads/2020/04/2020-04-Frontline-Workers.pdf
Rihl, J. (2020, May 2). Is staying home during the pandemic a luxury? Here’s what Pittsburgh-area data shows. PublicSource. Retrieved July 30, 2020, from https://www.publicsource.org/is-staying-home-during-the-pandemic-a-luxury-heres-what-pittsburgh-area-data-shows/
Saphores, J., & Xu, L. (2020). E-shopping changes and the state of E-grocery shopping in the US—Evidence from national travel and time use surveys. Research in Transportation Economics, (January), 100864. https://doi.org/10.1016/j.retrec.2020.100864
Shen, X., Zhou, Y., Jin, S., & Wang, D. (2020). Spatiotemporal influence of land use and household properties on automobile travel demand. Transportation Research Part D: Transport and Environment, 84, 102359. https://doi.org/10.1016/j.trd.2020.102359
TCRP. (2013). Transit capacity and quality of service manual (3rd ed.). Washington, DC: Transportation Research Board. https://doi.org/10.17226/24766
Teixeira, J. F., & Lopes, M. (2020). The link between bike sharing and subway use during the COVID-19 pandemic: The case-study of New York’s Citi Bike. Transportation Research Interdisciplinary Perspectives, 6, 100166. https://doi.org/10.1016/j.trip.2020.100166
Wood, S. (2003). Thin plate regression splines. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 65(1), 95–114. https://doi.org/10.1111/1467-9868.00374
Wood, S. (2017). Generalized additive models: An introduction with R. Boca Raton, FL: CRC press.
Wood, S. (2018). Mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation and GAMMs by REML/PQL. Retrieved from https://stat.ethz.ch/R-manual/R-devel/library/mgcv/html/mgcv-package.html
Wood, S. (2020). Smooth.terms function, R Documentation. Retrieved July 30, 2020, from https://www.rdocumentation.org/packages/mgcv/versions/1.8-31/topics/smooth.terms
