Ridesharing and motor vehicle crashes: a spatial ecological case-crossover study of trip-level data

Abstract

BackgroundRidesharing services (eg, Uber, Lyft) have facilitated over 11 billion trips worldwide since operations began in 2010, but the impacts of ridesharing on motor vehicle injury crashes are largely unknown.—MethodsThis spatial ecological case-cross over used highly spatially and temporally resolved trip-level rideshare data and incident-level injury crash data for New York City (NYC) for 2017 and 2018. The space-time units of analysis were NYC taxi zone polygons partitioned into hours. For each taxi zone-hour we calculated counts of rideshare trip origins and rideshare trip destinations. Case units were taxi zone-hours in which any motor vehicle injury crash occurred, and matched control units were the same taxi zone from 1 week before (−168 hours) and 1 week after (+168 hours) the case unit. Conditional logistic regression models estimated the odds of observing a crash (separated into all injury crashes, motorist injury crashes, pedestrian injury crashes, cyclist injury crashes) relative to rideshare trip counts. Models controlled for taxi trips and other theoretically relevant covariates (eg, precipitation, holidays).ResultsEach additional 100 rideshare trips originating within a taxi zone-hour was associated with 4.6% increased odds of observing any injury crash compared with the control taxi zone-hours (OR=1.046; 95% CI 1.032 to 1.060). Associations were detected for motorist injury and pedestrian injury crashes, but not cyclist injury crashes. Findings were substantively similar for analyses conducted using trip destinations as the exposure of interest.ConclusionsRidesharing contributes to increased injury burden due to motor vehicle crashes, particularly for motorist and pedestrian injury crashes at trip nodes.

Ridehailing and alcohol-involved traffic fatalities in the United States: The average and heterogeneous association of uber

“…Whereas much of the focus of the report is on documenting the extent of sexual assault occurring in Uber rides and by Uber drivers, Uber went further in the report to investigate the extent of motor vehicle fatalities involving Uber. They find that the rate of traffic fatalities involving an Uber driver, per vehicle mile travelled, is actually half of the rate in the total population [24]. Hence, whereas we find that traffic fatalities, including weekend/ holiday as well as alcohol-related fatalities, increased in population dense and urban centered areas, the increases do not necessarily involve Uber drivers.…”

“…To explain the lack of a main effect of Uber's availability yet significant interaction associations with population density and urbanicity, we first note that Uber recently released its first ever Safety Report [24]. Whereas much of the focus of the report is on documenting the extent of sexual assault occurring in Uber rides and by Uber drivers, Uber went further in the report to investigate the extent of motor vehicle fatalities involving Uber.…”

Ride-Hailing and Road Traffic Crashes: A Critical Review

“…In several studies in the United States (8,13,18) and the United Kingdom (30), authors found Uber's presence was not associated with crash fatalities; but authors of the studies from Brazil (25), Chile (26)(27)(28), South Africa (29), and 2 from the United States (15,22) found the presence of ride-hailing services were negatively associated with crash fatalities. In another analysis of US cities (19) and in a small-area study in New York City (20), researchers found positive relationships for injury and fatal crashes. Attention has turned in recent years to understanding how different researchers using comparable analytic methods, sometimes in the same geographic settings, could arrive at such divergent results.…”

“…Brazil and Kirk's work is an instructive example of the state of the science, because most studies published in the intervening 5 years have used similar data and methods to examine similar outcomes. Most were set in the United States (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24), apart from a handful of others conducted in Brazil (25), Chile (26)(27)(28), South Africa (29), Spain (30), and the United Kingdom (31). Other authors also examined associations with overall injury or fatal crash incidence (13, 15, 16, 18-23, 25-27, 29, 31) or with alcohol-involved injury or fatal crash incidence (10-15, 18, 22, 24, 26, 27, 30).…”

“…In epidemiologic terms, the presence of more vehicles will produce increases in expected crash incidence because of the greater size of the population at risk, which, in turn, will produce increases in the number of expected injuries and deaths (33). Researchers have accounted for road traffic volume methodologically by carefully selecting space-time units in a way that minimizes bias due to unknown denominators (20), or statistically by using estimated road traffic volume (21) or large spacetime units in which conditioning through model or variable specification can account for most of the variation in vehicular traffic flow (8,31). However, relationships between ride-hailing and vehicular traffic volume may not be dose responsive, because greater volume is negatively related to average speed, because of congestion.…”