Disparities in resilience and recovery of ridesourcing usage during COVID-19

The COVID-19 pandemic has impacted ridesourcing services dramatically, but empirical research on disparities in the resilience and recovery of ridesourcing has been scarce. To address this literature gap, we used ridesourcing trip data in Chicago to create two time series: one for Census tract-level ridesourcing usage (including pickups and dropoffs) and the other for linkages between origin and destination (OD) pairs. We performed time-series clustering analyses that integrated manifold learning and Gaussian Mixture Modeling to optimize the number of clusters for high-dimensional time-series data. The tract-level usage can be grouped into three clusters, and the OD-pair linkages can be grouped into six clusters. We examined the spatial patterns of the tract-level usage clusters and the OD-pair linkage clusters. Furthermore, we estimated a multinomial logit regression model to examine the relationships between clusters and land use, built environment, and sociodemographic factors. Our results suggested that the share of residential land use had a positive association with high resilience and fast recovery of ridesourcing usage. Limited transportation accessibility and a lack of alternative transportation modes were also associated with high resilience and fast recovery of ridesourcing usage. Trips that linked dense employment centers were less likely to be made during the pandemic. Census tracts with a greater share of minorities or a higher poverty rate tended to generate more ridesourcing trips during the pandemic.