# Changes in Commuter Behavior from COVID-19 Lockdowns in the Atlanta Metropolitan Area

arXiv: https://arxiv.org/abs/2302.13512 · HTML: https://mlsanity.com/paper/2302.13512/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Exploring universal patterns in human home-work commuting from mobile phone data](https://mlsanity.com/paper/1311.2911/) (2013)
- [Resiliency of On-Demand Multimodal Transit Systems During a Pandemic](https://mlsanity.com/paper/2101.05395/) (2021)
- [Bilevel Optimization and Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale Transit Systems](https://mlsanity.com/paper/2212.03460/) (2022)
- [Public Transit for Special Events: Ridership Prediction and Train Optimization](https://mlsanity.com/paper/2106.05359/) (2021)

## Abstract (arXiv)

This paper analyzes the impact of COVID-19 related lockdowns in the Atlanta, Georgia metropolitan area by examining commuter patterns in three periods: prior to, during, and after the pandemic lockdown. A cellular phone location dataset is utilized in a novel pipeline to infer the home and work locations of thousands of users from the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The coordinates derived from the clustering are put through a reverse geocoding process from which word embeddings are extracted in order to categorize the industry of each work place based on the workplace name and Point of Interest (POI) mapping. Frequencies of commute from home locations to work locations are analyzed in and across all three time periods. Public health and economic factors are discussed to explain potential reasons for the observed changes in commuter patterns.