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Ridesharing and motor vehicle crashes: a spatial ecological case-crossover study of trip-level data
  1. Christopher N Morrison1,2,
  2. Christina Mehranbod1,
  3. Muhire Kwizera3,
  4. Andrew G Rundle1,
  5. Katherine M Keyes1,
  6. David K Humphreys4
  1. 1Department of Epidemiology, Mailman School of Public Health, Columbia University, New York city, New York, USA
  2. 2Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
  3. 3Department of Biostatistics, Mailman School of Public Health, Columbia University, New York city, New York, USA
  4. 4Department of Social Policy and Intervention, Oxford University, Oxford, Oxfordshire, UK
  1. Correspondence to Dr Christopher N Morrison, Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA; cm3820{at}


Background Ridesharing 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.—

Methods This 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).

Results Each 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.

Conclusions Ridesharing contributes to increased injury burden due to motor vehicle crashes, particularly for motorist and pedestrian injury crashes at trip nodes.

  • motor vehicle—non-traffic
  • pedestrian
  • distraction
  • motor vehicle—occupant

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  • Funding This study was funded by the National Institute for Alcohol Abuse and Alcoholism (K01AA026327) and the Centers for Disease Control and Prevention (R49-CE003094).

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  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.