Work

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

Year: 2020

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2010.09648

Authors: 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

Institution: New York University

Language: English

Topic: Transportation Planning and Optimization

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: green

OpenAlex ID: W3093340122

Cites: 7

Cited by: 13

Related to: 10