Highlights Use Big Data Analytics to identify crash risk factors for ride-hailing drivers. Significant risk factors include passenger rating, long shifts, peak-hour driving. Use Poisson Generalized Additive Model to accommodate nonlinear effect. Use the SHAP method to assess the impact of risk factors. Operational characteristics are valuable for assessing ride-hailing driver crash risk.
Abstract Ride-hailing services, which have become increasingly prevalent in the last decade, provide an efficient travel mode by matching drivers and travelers via smartphone apps. Ride-hailing services enable millions of non-traditional taxi drivers to provide travel services, but may also raise safety concerns due to heterogeneity in the driver population. This study evaluated crash risk factors for ride-hailing drivers, including driving history and ride-hailing operational characteristics, using a sample of 189,815 drivers. We utilized the Poisson generalized additive model to accommodate for the potential nonlinear relationship between crash rate and risk factors. Results showed that crash history, the percentage of long-shift bookings, driving distance, operations during peak hours, years of being a ride-hailing driver, and passenger rating were significantly associated with crash risk. Several factors showed nonlinear relationships with crash risk. We adopted the SHapley Additive exPlanation (SHAP) method to assess and visualize the impact of each risk factor. The results indicated that passenger average rating, total driving distance, and crash history were the leading contributing factors. The findings of this study provide critical information for the development of safety countermeasures, driver education programs, as well as safety regulations for the ride-hailing industry.
Driving safety assessment for ride-hailing drivers
2020-04-21
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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