Suggested Citation:
Gulhare, Siddhartha, Ran Sun, Keita Makino, Krishna Behara, David S. Bunch, Giovanni Circella (2025)

Investigating Travel Demand Heterogeneity During and After the Pandemic in the Northern California Megaregion: A Data-Driven Analysis of Origin–Destination Structural Patterns

The study delves into the complexities of travel disruption and recovery during and after the COVID-19 pandemic. Using a data-driven methodology, we explore spatial-temporal patterns across regions by times of the day, weekdays/weekends, and trip purposes. Using passively collected location-based data from January 2019 to October 2021 in the Northern California Megaregion, our analysis compares travel patterns through the structural similarity of origin-destination (OD) matrices. Introducing the concept of a “local sliding geographical window” based on natural trip flow, the study identifies various impacts of the pandemic on travel demand including but not limited to (a) trip volume and recovery (e.g., weekday trips dropped by 47% in April 2020, gradually recovering already by October 2021); (b) impact on home-based work and other trips which were significantly disrupted on weekdays compared with non-home-based; (c) OD pattern changes (e.g., all sub-regions experienced significant changes, but the San Francisco Bay area faced the maximum disruptions); (d) gradual recovery with regional variations (e.g., San Francisco lagged in its travel activity recovery but this improved after April 2021, whereas the Northern San Joaquin Valley recovered fastest); (e) disruption and recovery linked to socioeconomic factors (e.g., parts of San Francisco, characterized by higher income, white-collar jobs, faced maximum disruption, whereas the Northern San Joaquin Valley, with a higher proportion of blue-collar workers, experienced the least disruption); and (f) differential recovery rates across and within regions, with areas rich in white-collar jobs showing slower recovery for work trips compared with areas with a higher proportion of blue-collar jobs.