What mobile phone data reveal about mobility patterns of teleworkers

Abstract

In a short period, the COVID-19 pandemic has transformed telework into a common practice for a significant portion of the workforce. This shift has profound implications for land use, urban development, and transportation. Traditional survey-based methods for tracking these changes are struggling to keep pace with the rapidity of this transformation. Here, we propose a method to identify different types of workers using mobile phone data, enabling a detailed examination of the correlation between work arrangements, mobility patterns and key socio-demographic attributes. By applying a hierarchical clustering algorithm to features extracted from a mobile phone dataset, six different worker types are identified and their validity is confirmed using different approaches. We find teleworkers tend to travel slower than regular workers but faster than non-workers. They also travel shorter distances to reach their primary activity locations than regular workers, but longer distances to other activity locations than both regular and non-workers. Our regression analysis further reveals that, largely in agreement with findings in literature, racial minority and low income groups are less likely to telework.