Start Date
11-12-2016 12:00 AM
Description
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 combines trip-level data about Uber, taxi, and bike share usage in New York City with turnstile data about subway 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 crowd-based systems’ potential to smooth unexpected demand surges. Our ongoing work studies how substitution varies with socioeconomic indicators, and how substitution towards ridesharing compares to traditional taxi and bike sharing. We hope to lay a data-driven foundation to better understand how sharing economy alternatives relate to existing and future capital-intensive transit systems.
Recommended Citation
Hoffmann, Katherine; Ipeirotis, Panos; and Sundararajan, Arun, "Ridesharing and the Use of Public Transportation" (2016). ICIS 2016 Proceedings. 14.
https://aisel.aisnet.org/icis2016/DataScience/Presentations/14
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 combines trip-level data about Uber, taxi, and bike share usage in New York City with turnstile data about subway 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 crowd-based systems’ potential to smooth unexpected demand surges. Our ongoing work studies how substitution varies with socioeconomic indicators, and how substitution towards ridesharing compares to traditional taxi and bike sharing. We hope to lay a data-driven foundation to better understand how sharing economy alternatives relate to existing and future capital-intensive transit systems.