Modeling the impact of COVID-19 on transportation at later stage of the pandemic: A case study of Utah

References

• AASHTO (2020). Study: COVID-19 changing commuter traffic patterns – AASHTO journal. https://aashtojournal.org/2020/10/09/study-covid-19-changing-commuter-traffic-patterns/
   Google Scholar
• Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I. J., Harp, A., Irving, G., Isard, M., Jia, Y., Józefowicz, R., Kaiser, L., Kudlur, M., & Zheng, X. (2016). TensorFlow: Large-scale machine learning on heterogeneous distributed systems (CoRR, abs/1603.0). http://arxiv.org/abs/1603.04467
   Google Scholar
• Centers for Disease Control and Prevention (CDC). (2021). When You’ve Been Fully Vaccinated. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/fully-vaccinated.html
   Google Scholar
• Chollet, F. (2015). Keras. In GitHub repository. GitHub.
   Google Scholar
• Christidis, P., Christodoulou, A., Navajas-Cawood, E., & Ciuffo, B. (2021). The post-pandemic recovery of transport activity: Emerging mobility patterns and repercussions on future evolution. Sustainability 2021, Vol. 13, Page 6359, 13(11), 6359. https://doi.org/10.3390/su13116359
   Google Scholar
• Data61, C. (2018). StellarGraph machine learning library. In GitHub Repository. GitHub.
   Google Scholar
• Defferrard, M., Bresson, X., & Vandergheynst, P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems, 29, 3844–3852. https://arxiv.org/abs/1606.09375v3
   Google Scholar
• Glaeser, E. L., Gorback, C., & Redding, S. J. (2020). How much does covid-19 increase with mobility? evidence from New York and four other U.S. cities. https://doi.org/10.3386/W27519
   Google Scholar
• Hochreiter, S., Schmidhuber, J., Gambardella, L. M., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
   PubMed Web of Science ®Google Scholar
• Horsley, S. (2020). GDP drops at 32.9% rate, the worst U.S. contraction ever : Coronavirus updates : NPR. https://www.npr.org/sections/coronavirus-live-updates/2020/07/30/896714437/3-months-of-hell-u-s-economys-worst-quarter-ever
   Google Scholar
• Jiang, W., & Luo, J. (2021). Graph neural network for traffic forecasting: A survey. https://arxiv.org/abs/2101.11174v2
   Google Scholar
• Katrakazas, C., Michelaraki, E., Sekadakis, M., & Yannis, G. (2020). A descriptive analysis of the effect of the COVID-19 pandemic on driving behavior and road safety. Transportation Research Interdisciplinary Perspectives, 7, 100186. https://doi.org/10.1016/J.TRIP.2020.100186
   PubMedGoogle Scholar
• Kim, K. (2021). Impacts of COVID-19 on transportation: Summary and synthesis of interdisciplinary research. Transportation Research Interdisciplinary Perspectives, 9, 100305. https://doi.org/10.1016/J.TRIP.2021.100305
   PubMedGoogle Scholar
• Le, Q. V., Jaitly, N., & Hinton, G. E. (2015). A simple way to initialize recurrent networks of rectified linear units. https://arxiv.org/abs/1504.00941v2
   Google Scholar
• Li, X., Zheng, Y., Alshehri, M. D., Hai, L., Balasubramanian, V., Zeng, M., & Nie, G. (2021). A hierarchical framework for interactive behaviour prediction of heterogeneous traffic participants based on graph neural network. IEEE Transactions on Intelligent Transportation Systems, 23(7), 9102–9114. https://doi.org/10.1109/TITS.2021.3090851
   Web of Science ®Google Scholar
• Mathieu, E., Ritchie, H., Ortiz-Ospina, E., Roser, M., Hasell, J., Appel, C., Giattino, C., & Rodés-Guirao, L. (2021). A global database of COVID-19 vaccinations. Nature Human Behaviour, 5, 947–953. https://doi.org/10.1038/S41562-021-01122-8
   Web of Science ®Google Scholar
• Maze, T. H., Agarwal, M., & Burchett, G. (2006). Whether weather matters to traffic demand, traffic safety, and traffic operations and flow. Transportation Research Record: Journal of the Transportation Research Board, 1948(1), 170–176., https://doi.org/10.1177/0361198106194800119 https://doi.org/10.1177/0361198106194800119
   Google Scholar
• National Centres for Environmental Information (NCEI). (2020). Find a Station | Data Tools | Climate Data Online (CDO) | National Climatic Data Center (NCDC). https://www.ncdc.noaa.gov/cdo-web/datatools/findstation
   Google Scholar
• Rothengatter, W., Zhang, J., Hayashi, Y., Nosach, A., Wang, K., & Oum, T. H. (2021). Pandemic waves and the time after Covid-19 – Consequences for the transport sector. Transport Policy, 110, 225–237. https://doi.org/10.1016/J.TRANPOL.2021.06.003
   PubMed Web of Science ®Google Scholar
• Shamshiripour, A., Rahimi, E., Shabanpour, R., & Mohammadian, A. (2020). How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Transportation Research Interdisciplinary Perspectives, 7, 100216. https://doi.org/10.1016/J.TRIP.2020.100216
   PubMedGoogle Scholar
• Shapiro, A. H., Sudhof, M., & Wilson, D. (2017). Measuring news sentiment. Federal Reserve Bank of San Francisco, Working Paper 2017-01. Available at https://doi.org/10.24148/wp2017-01
   Google Scholar
• Tang, J., & Zeng, J. (2022). Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data. Computer-Aided Civil and Infrastructure Engineering, 37(1), 3–23. https://doi.org/10.1111/mice.12688
   Web of Science ®Google Scholar
• The State of Utah. (2021a). Case counts: coronavirus. Coronavirus.Utah.Gov. https://coronavirus.utah.gov/case-counts/
   Google Scholar
• The State of Utah. (2021b). Special orders | coronavirus. https://coronavirus.utah.gov/special-orders/
   Google Scholar
• Utah Department of Transportation. (2019). PeMS @ UDOT. https://udot.iteris-pems.com/?fwy=15&dir=S&dnode=Freeway&content=elv&tab=stations&pagenum=4
   Google Scholar
• Utah Department of Workforce Services. (2021). Seasonally adjusted unemployment rates by county. https://jobs.utah.gov/wi/update/une/
   Google Scholar
• Xiong, X., Ozbay, K., Jin, L., & Feng, C. (2020). Dynamic origin–destination matrix prediction with line graph neural networks and kalman filter. Transportation Research Record: Journal of the Transportation Research Board, 2674(8), 491–503. 2674(8), 491–503. https://doi.org/10.1177/0361198120919399
   Web of Science ®Google Scholar
• Yuan, J., Abdel-Aty, M., Gong, Y., & Cai, Q. (2019). Real-time crash risk prediction using long short-term memory recurrent neural network. Transportation Research Record, 2673(4), 314–326. https://doi.org/10.1177/0361198119840611.
   Web of Science ®Google Scholar
• Yuan, Y., Zhang, Z., Yang, X. T., & Zhe, S. (2021). Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation. Transportation Research Part B: Methodological, 146, 88–110. https://doi.org/10.1016/j.trb.2021.02.007
   Web of Science ®Google Scholar
• Zhang, J., & Lee, J. (2021). Interactive effects between travel behaviour and COVID-19: A questionnaire study. Transportation Safety and Environment, 3(2), 166–177. https://doi.org/10.1093/tse/tdab003
   Google Scholar
• Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., & Li, H. (2020). T-GCN: A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 21(9), 3848–3858. https://doi.org/10.1109/TITS.2019.2935152
   Web of Science ®Google Scholar
• Zhu, J., Han, X., Deng, H., Tao, C., Zhao, L., Tao, L., & Li, H. (2020). KST-GCN: A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting. https://arxiv.org/abs/2011.14992v1
   Google Scholar