Ridesharing and the Use of Public Transportation

We investigate the effects of mobile-sourced ridesharing via platforms like Uber, Lyft, and Didi Chuxing on the use of public transit systems. Our study uses trip-level data about Uber usage in New York City, turnstile data about subway usage, and trip-level data about taxicab and shared bike usage. We find that on the surface, ridesharing and subway usage are positively correlated. Exploiting a series of exogenous shocks to the system – the closing of subway stations – to better isolate substitution effects, our preliminary results suggest that the average shock results in an increase of over 30% in the use of ridesharing, highlighting the potential for crowd-based systems to serve as infrastructure that helps smooth unexpected supply and demand surges. Our ongoing work studies how these substitution patterns vary with neighborhood socioeconomic indicators, and how substitution towards mobile-hailed ridesharing compares to traditional taxi and bike sharing. We hope to lay a data-driven foundation to better understand how sharing economy alternatives substitute and complement existing and future capital-intensive transit systems, and to provide a more judicious basis for assessing impacts on different population segments.