Objective: Rideshare companies such as Uber and Lyft have substantially changed transportation markets in the United States and globally. The aim of this study was to examine whether ridesharing is associated with reductions in alcohol-involved crashes.
Method: This case-series study used highly spatially and temporally resolved trip-level rideshare data and motor vehicle crash data from the Chicago Data Portal from November 2018 to December 2019. The units of analysis were motor vehicle crashes in Chicago. Events of interest were 962 crashes that police indicated were alcohol involved. The comparison group was 962 non-alcohol-involved crashes that occurred in the same census tract, matched 1:1. The exposure of interest was the density per square mile of rideshare trips that were in progress at the time of the crash, calculated using a kernel density function around the estimated route paths of active trips. A conditional logistic regression compared alcohol involvement to rideshare trip density while adjusting for matching and relevant time-varying covariates (taxi trips, precipitation, temperature, holidays).
Results: Mean rideshare trip density was 69.0 per square mile (SD = 129.7) at the time and location of alcohol-involved crashes and 105.7 per square mile (SD = 192.6) at the time and location of non-alcohol-involved crashes. After controlling for covariates, the conditional logistic regression model identified that a standard deviation increase in rideshare trips per square mile at the crash location was associated with 23% decreased odds that the crash location was alcohol involved (odds ratio = 0.771; 95% confidence interval [0.594, 0.878]).
Conclusions: Ridesharing may replace motor vehicle trips by alcohol-impaired drivers.