Disparities in ridesourcing demand for mobility resilience: A multilevel analysis of neighborhood effects in Chicago, Illinois

Mobility resilience refers to the ability of individuals to complete their desired travel despite unplanned disruptions to the transportation system. The potential of new on-demand mobility options, such as ridesourcing services, to fill unpredicted gaps in mobility is an underexplored source of adaptive capacity. Applying a natural experiment approach to newly released ridesourcing data, we examine variation in the gap-filling role of on-demand mobility during sudden shocks to a transportation system by analyzing the change in use of ridesourcing during unexpected rail transit service disruptions across the racially and economically diverse city of Chicago. Using a multilevel mixed model, we control not only for the immediate station attributes where the disruption occurs, but also for the broader context of the community area and city quadrant in a three-level structure. Thereby the unobserved variability across neighborhoods can be associated with differences in factors such as transit ridership, or socio-economic status of residents, in addition to controlling for station level effects. Our findings reveal that individuals use ridesourcing as a gap-filling mechanism during rail transit disruptions, but there is strong variation across situational and locational contexts. Specifically, our results show larger increases in transit disruption responsive ridesourcing during weekdays, nonholidays, and more severe disruptions, as well as in community areas that have higher percentages of White residents and transit commuters, and on the more affluent northside of the city. These findings point to new insights with far-reaching implications on how ridesourcing complements existing transport networks by providing added capacity during disruptions but does not appear to bring equitable gap-filling benefits to low-income communities of color that typically have more limited mobility options.

💡 Research Summary

This paper investigates how on‑demand ridesourcing services (Uber, Lyft, Via) function as a gap‑filling mobility option during unexpected rail transit disruptions in Chicago, and whether the resulting mobility resilience is equitably distributed across neighborhoods. The authors adopt a natural‑experiment design, identifying 28 significant CTA rail service interruptions between November 2018 and October 2019 through systematic media searches. For each disruption, ridesourcing trips that originated within a 0.25‑mile walking radius of the affected station are extracted from the city’s open‑data portal, which contains over 152 million trips for the study period. A four‑week baseline (two weeks before and after the event, matched by day‑of‑week and time‑of‑day) is constructed to estimate the expected ridesourcing volume absent the disruption.

To capture hierarchical influences, a three‑level multilevel mixed‑effects model is estimated. Level 1 (station) controls for disruption characteristics such as duration, peak‑hour status, weekday versus weekend, holiday, temperature, shuttle deployment, and whether the cause was a medical emergency. Level 2 (community area) incorporates socio‑demographic and transportation variables: percentage of White residents, median household income, population density, proportion of commuters who use transit, number of bus and Divvy bike stations, and other built‑environment attributes. Level 3 (city quadrant) groups the 77 community areas into North, Central, South, and West sectors to account for broader spatial heterogeneity. Random intercepts at each level allow unobserved variation to be partitioned across the three scales.

Key findings: (1) Ridesourcing demand rises markedly during disruptions, with larger spikes on weekdays, non‑holidays, and when disruptions last longer than two hours. The average increase ranges from 12 % to 35 % relative to the baseline, indicating a rapid mode‑shift response. (2) Community‑level variables exert the strongest influence: higher percentages of White residents and higher transit‑commuter shares are positively associated with larger ridesourcing surges, and these effects interact with station‑level severity. In neighborhoods where a larger share of residents rely on public transit, a disruption triggers a more pronounced ridesourcing response. (3) Quadrant analysis reveals that the affluent North side experiences the greatest demand spikes, whereas the South and West quadrants—characterized by lower income and higher concentrations of people of color—show modest or statistically insignificant changes. Variance decomposition shows that roughly 55 % of the total variation in ridesourcing response is explained at the community level, 30 % at the station level, and 15 % at the quadrant level. (4) The fixed‑effects coefficients indicate that temporal factors (weekday, non‑holiday) have a larger magnitude than the duration of the disruption itself, underscoring the importance of commuters’ schedule rigidity.

Policy implications are drawn from these inequities. The authors recommend that transit agencies negotiate pre‑emptive agreements with ridesourcing providers to guarantee vehicle supply and fare subsidies in low‑income, minority‑majority neighborhoods during service outages. Real‑time disruption alerts should be multilingual and complemented by offline communication channels (e.g., station signage, community centers) to reduce information gaps. Integrating ridesourcing into a Mobility‑as‑a‑Service (MaaS) platform—offering discounted fares, shared payment mechanisms, and seamless connections to public transit—could improve accessibility for socially vulnerable groups.

In conclusion, while ridesourcing does serve as an effective resilience mechanism during short‑term rail disruptions, its benefits are unevenly distributed, reinforcing existing spatial and racial inequities in Chicago’s mobility system. Future work should examine longer‑term disruptions, the persistence of mode‑shift behavior, and the role of other on‑demand modes (e‑scooters, bike‑share) to develop a more comprehensive framework for equitable mobility resilience.