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, 187 (2), 224-232

Ridesharing and Motor Vehicle Crashes in 4 US Cities: An Interrupted Time-Series Analysis

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Ridesharing and Motor Vehicle Crashes in 4 US Cities: An Interrupted Time-Series Analysis

Christopher N Morrison et al. Am J Epidemiol.

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.

Keywords: accidents; alcohol drinking; interrupted time-series analysis; motor vehicles; traffic; transportation.

Figures

Figure 1.
Figure 1.
Weekly time-series plots for the proportion of all injury crashes that were alcohol-involved in Portland, Oregon (A); San Antonio, Texas (B); and Reno, Nevada (C); January 1, 2013, to June 30, 2016. Standard notation is provided to describe (autoregressive integrated moving average (ARIMA)) model specification, ARIMA(p,d,q), where p describes the autoregressive term, d describes the difference term, and q describes the moving average term. We also provide a Q statistic at a lag of 24 units, where lower values indicate better model fit. The predicted values for Portland are obtained from an interrupted time-series model with an abrupt permanent association in the week of April 21, 2015 (ARIMA(0,1,1); Q(24 lags) = 17.6). The predicted values for San Antonio are from a model with an abrupt temporary association in the week of April 1, 2015, and an abrupt permanent association in the week of October 1, 2015 (ARIMA(0,1,1); Q(24 lags) = 17.3). The predicted values for Reno are from a null model (i.e., with no interruptions) (ARIMA(0,1,1); Q(24 lags) = 22.4).
Figure 2.
Figure 2.
Weekly time-series plots for counts of all injury crashes in Las Vegas, Nevada (A); Portland, Oregon (B); San Antonio, Texas (C); and Reno, Nevada (D); January 1, 2013, to June 30, 2016. Standard notation is provided to describe (autoregressive integrated moving average (ARIMA)) model specification, ARIMA(p,d,q), where p describes the autoregressive term, d describes the difference term, and q describes the moving average term. We also provide a Q statistic at a lag of 24 units, where lower values indicate better model fit. The predicted values for Las Vegas are from a time-series model with no interruptions (ARIMA(0,1,1)(0,1,1)52; Q(24 lags) = 19.3). The predicted values for Portland are from a null model (ARIMA(0,1,1)(0,1,0)52; Q(24 lags) = 14.5). The predicted values for San Antonio are from a model with an abrupt permanent association in the week of October 1, 2015 (ARIMA(0,1,1); Q(24 lags) = 31.2). The predicted values for Reno are from a null model (ARIMA(0,1,1)(0,1,0)52; Q(24 lags) = 31.1).

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