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Public transit in the U.S. has an unsettled future. The onset of the COVID-19 pandemic saw a dramatic decline in transit ridership, with agency operations, and user perceptions of safety changing significantly. However, one new factor beyond the control of agencies is playing an outsized role in transit ridership: the shifting employment patterns in the hybrid work era. Indeed, a lasting and widespread adoption of telework has emerged as a key determinant of individual transit behaviors. This study investigates the impact of teleworking on public transit ridership changes across the different transit services in the Chicago area during the pandemic, employing a random forest machine learning approach applied to large-scale survey data (n = 5637). The use of ensemble machine learning enables a data-driven investigation that is tailored for each of the three main transit service operators in Chicago (Chicago Transit Authority, Metra, and Pace). The analysis reveals that the number of teleworking days per week is a highly significant predictor of lapsed ridership. As a result, commuter-centric transit modes—such as Metra—saw the greatest declines in ridership during the pandemic. The study's findings highlight the need for transit agencies to adapt to the enduring trend of teleworking, considering its implications for future ridership and transportation equity. Policy recommendations include promoting non-commute transit use and addressing the needs of demographic groups less likely to telework. The study contributes to the understanding of how telework trends influence public transit usage and offers insights for transit agencies navigating the post-pandemic world. 

Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they affect travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examined emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data were matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcing’s role in Chicago’s mobility system. The goal was to investigate the systematic variations in patronage of ridehailing. K-prototypes was utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output was a classification of the data into several clusters called prototypes. Six ridesourcing prototypes were identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses the implications of the identified clusters related to affordability, equity, and competition with transit.