Safety efficiency assessment of online car-hailing drivers based on the nonparametric method

This study introduces a novel driver safety evaluation method for online car-hailing drivers, focusing on dangerous driving behaviors. By analyzing data from 97 drivers over one month, including the frequencies of dangerous behaviors and driving distances, we develop a methodological framework integrating Data Envelopment Analysis (DEA) and the Super-Efficiency DEA model. This framework dynamically links dangerous driving behaviors to driving risk, calculates driver safety efficiency, and ranks driving safety. It also offers behavioral improvement advice to poorly performing drivers based on slack variables. Compared to traditional models such as EWM-TOPSIS and Critic-TOPSIS, our method exhibits superior stability and provides a more individualized and objective assessment. The DEA model effectively ranks driver safety performance, highlighting its advantages over TOPSIS in handling multiple inputs and outputs without predefined weights. This research offers valuable insights for online car-hailing companies to identify high-risk drivers and implement targeted safety training programs.