Abstract

The COVID-19 pandemic significantly impacted the transportation sector, altering travel demand patterns and posing challenges for local systems. Evidence of spatial heterogeneity underscores the necessity for a comprehensive understanding of these disruptions. Origin–destination (OD) matrices are generally used to compare travel patterns. Direct observations like smartphone data to construct OD matrices may limit causality in trip distribution, emphasizing the need for a methodology enabling comparison of travel patterns and exploration of factors contributing to this heterogeneity. To this end, this study develops a novel two-phase methodology. The first phase involved capturing heterogeneity in the weekly progression of zonal trip-generation patterns (via structural similarity of OD matrices) and then clustering them together based on similarity. The second phase involved examining the factors influencing cluster membership of zones. We demonstrated the proof-of-concept using two case studies: home-based work trips on weekdays and home-based other trips on weekends. The case studies focused on the Northern California Megaregion. The data used in the first phase include passively collected mobile phone data. The second phase used data on explanatory variables (e.g., mean household income, employment density, the share of white- and blue-collar workers and half-mile transit accessibility) for the multinomial logit model. This additional data to augment the data set is sourced from American Community Survey five-year estimates and the US Environmental Protection Agency. This study uniquely applies a novel methodology to two case studies, showcasing how insights into factors driving travel pattern changes can assist local and regional policymakers in optimizing resource allocation, particularly for public transportation.