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arXiv · 2010.09648

Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19

Ding Wang·Fan Zuo·Jingqin Gao·Yueshuai He·Zilin Bian·Suzana Duran Bernardes·Chaekuk Na·Jingxing Wang·John Petinos·Kaan Ozbay·Joseph Y. J. Chow·Shri Iyer·Hani Nassif·Xuegang Jeff Ban
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Abstract

The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. This edition of the white paper updates travel trends and highlights an agent-based simulation model's results to predict the impact of proposed phased reopening strategies. It also introduces a real-time video processing method to measure social distancing through cameras on city streets.

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Ding Wang, Fan Zuo, Jingqin Gao, Yueshuai He, Zilin Bian, Suzana Duran Bernardes, Chaekuk Na, Jingxing Wang, John Petinos, Kaan Ozbay, Joseph Y. J. Chow, Shri Iyer, Hani Nassif, Xuegang Jeff Ban. 2020-09-23. Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19. https://arxiv.org/abs/2010.09648

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