Mao Huiying, Deng Xinwei, Jiang Honggang, Shi Liang, Li Hao, Tuo Liheng, Shi Donghai, Guo Feng
Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA; Didi Chuxing Technology Co., Beijing, China.
Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA.
Accid Anal Prev. 2021 Jan;149:105574. doi: 10.1016/j.aap.2020.105574. Epub 2020 Jul 29.
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.
在过去十年中日益普及的网约车服务，通过智能手机应用程序将司机和乘客进行匹配，提供了一种高效的出行方式。网约车服务使数百万非传统出租车司机能够提供出行服务，但由于司机群体的异质性，也可能引发安全担忧。本研究使用189,815名司机的样本，评估了网约车司机的撞车风险因素，包括驾驶历史和网约车运营特征。我们使用泊松广义相加模型来处理撞车率与风险因素之间潜在的非线性关系。结果表明，撞车历史、长班次预订百分比、驾驶距离、高峰时段运营、网约车司机年限以及乘客评分与撞车风险显著相关。几个因素与撞车风险呈现非线性关系。我们采用SHapley Additive exPlanation（SHAP）方法来评估和可视化每个风险因素的影响。结果表明，乘客平均评分、总驾驶距离和撞车历史是主要影响因素。本研究结果为制定安全对策、司机教育计划以及网约车行业安全法规提供了关键信息。
