Author: Wang, Ding; Zuo, Fan; Gao, Jingqin; He, Yueshuai; Bian, Zilin; Bernardes, Suzana Duran; Na, Chaekuk; Wang, Jingxing; Petinos, John; Ozbay, Kaan; Chow, Joseph Y.J.; Iyer, Shri; Nassif, Hani; Ban, Xuegang Jeff
Title: Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19 Cord-id: hwfloaud Document date: 2020_9_23
ID: hwfloaud
Snippet: 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.
Document: 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.
Search related documents:
Co phrase search for related documents, hyperlinks ordered by date