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

arXiv: https://arxiv.org/abs/2010.09648 · HTML: https://mlsanity.com/paper/2010.09648/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review](https://mlsanity.com/paper/2104.04059/) (2021)

## Abstract (arXiv)

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.