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.
@article{arxiv.2010.09648,
title = {Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19},
author = {Ding Wang and Fan Zuo and Jingqin Gao and Yueshuai He and Zilin Bian and Suzana Duran Bernardes and Chaekuk Na and Jingxing Wang and John Petinos and Kaan Ozbay and Joseph Y. J. Chow and Shri Iyer and Hani Nassif and Xuegang Jeff Ban},
journal= {arXiv preprint arXiv:2010.09648},
year = {2020}
}