With the explosive growth of Uber and other mobility service providers, their influences on urban mobility have attracted attention from both researchers and policy circles alike, yet few quantitative studies have been conducted on the topic. Using Uber pickup data in New York City in 2014, this article investigates the spatiotemporal relationship between Uber and public transit using buffer analysis and spatial cross-correlation analysis and assesses Uber’s impact on urban transportation equity with the Gini coefficient and correlation analysis. Our results confirm previous arguments that Uber both complements and competes with public transit, but competition is more prevalent in New York City. Specifically, Uber competes with public transit during most hours of the day and in areas with good public transit coverage, whereas it complements public transit at midnight and in places with insufficient public transit services. The distribution of Uber services is highly unequal, and Uber’s role in improving transport equity is insignificant. Correlation analysis shows that there tend to be fewer Uber pickups in low-income areas, which diverges from previous studies suggesting that Uber serves low-income areas well. In addition, a weak negative correlation is detected between the number of Uber pickups and the percentage of minorities.

Estimating passengers’ door-to-door travel time, for journeys that combine walking and public transit, can be complex for large networks with many available path alternatives. Additional complications arise in tap-on only transit systems, where passengers alightings are not recorded. For one such system, the Chicago Transit Authority, this study compares three methods for estimating door-to-door travel time: assuming optimal path choice given scheduled service, as represented in the General Transit Feed Specification (GTFS); assuming optimal path choice given actually operated bus service, as recorded by automatic vehicle location systems; and using inferred path choices based on automated fare collection smartcard records, as processed with an origin-destination-interchange (ODX) inference algorithm. As expected, ODX-derived travel times are found to be longer than those derived from GTFS, indicating that purely schedule-based travel times underestimate the travel times that are actually available and experienced, which can be attributed primarily to suboptimal passenger route choice. These discrepancies additionally manifest in significant spatial variations, raising concerns about potential biases in travel time estimates that do not account for reliability. The findings bring about a more comprehensive understanding of the interactions between transit reliability and passenger behavior in transportation research. Furthermore, these discrepancies suggest areas of future research into the implications of systematic and behavioral assumptions implied by using conventional schedule-based travel time estimates.