Changes in Commuter Behavior from COVID-19 Lockdowns in the Atlanta Metropolitan Area
It addresses the problem of understanding how public health crises affect urban mobility for policymakers and researchers, but it is incremental as it applies existing methods to new data in a specific context.
This paper analyzed the impact of COVID-19 lockdowns on commuter patterns in the Atlanta metropolitan area by examining changes before, during, and after the pandemic, using a novel pipeline with cellular phone location data to infer home and work locations and categorize industries.
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.