Ridesharing and Motor Vehicle Crashes in 4 US Cities: An Interrupted Time-Series Analysis. Issue 2 (14th June 2017)

• Record Type: Journal Article
• Authors: Morrison, Christopher N; Jacoby, Sara F; Dong, Beidi; Delgado, M Kit; Wiebe, Douglas J
• Is Part Of: American journal of epidemiology. Volume 187:Issue 2(2018)
• Page Start: 224
• Page End: 232
• Publication Date: 2017-06-14
• Subjects: accidents -- alcohol drinking -- interrupted time-series analysis -- motor vehicles -- traffic -- transportation; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwx233
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: Uber, the world's largest ridesharing company, has reportedly provided over 2 billion journeys globally since operations began in 2010; however, the impact on motor vehicle crashes is unclear. Theoretically, ridesharing could reduce alcohol-involved crashes in locations where other modes of transportation are less attractive than driving one's own vehicle while under the influence of alcohol. We conducted interrupted time-series analyses using weekly counts of injury crashes and the proportion that were alcohol-involved in 4 US cities (Las Vegas, Nevada; Reno, Nevada; Portland, Oregon; and San Antonio, Texas). We considered that a resumption of Uber operations after a temporary break would produce a more substantial change in ridership than an initial launch, so we selected cities where Uber launched, ceased, and then resumed operations (2013–2016). We hypothesized that Uber's resumption would be associated with fewer alcohol-involved crashes. Results partially supported this hypothesis. For example, in Portland, Uber's resumption was associated with a 61.8% reduction (95% confidence interval: 38.7, 86.4) in the alcohol-involved crash rate (an absolute decrease of 3.1 (95% confidence interval: 1.7, 4.4) alcohol-involved crashes per week); however, there was no concomitant change in all injury crashes. Relationships between ridesharing and motor vehicle crashes differ between cities over time and may depend on specific local characteristics.