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

TL;DR

This study examines how COVID-19 lockdowns altered commuter behaviors in Atlanta using cellular data, clustering, and geocoding to analyze shifts in work and home locations across three pandemic phases.

Contribution

It introduces a novel pipeline combining cellular data, clustering, and geocoding to analyze commuter pattern changes during COVID-19 in Atlanta.

Findings

Significant reduction in commute frequency during lockdowns.

Changes in workplace industry categories over time.

Partial return to pre-pandemic commuting patterns post-lockdown.

Abstract

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.