A comparative analysis of risk factors influencing crash severity between full-time and part-time riding-hailing drivers in China

ABSTRACT

Ride-hailing is increasingly important in urban public transportation, yet research on its traffic safety remains limited. This study examined risk factors for crash severity among ride-hailing drivers, including demographics, financial burden, phone usage, fatigue, and risky driving behaviors. Data were collected from 2,182 drivers via a self-reported survey. Recognizing potential differences between full-time and part-time drivers, the data were divided accordingly, and two Bayesian network models were generated. The results indicated that both types of drivers suffer from heavy financial burdens and severe fatigue, with certain factors related to risky behaviors or phone usage increasing the likelihood of serious crashes. However, the specific risk factors leading to severe crashes varied between the two groups. Furthermore, the study confirmed that several combinations of risk factors exhibit nonlinear amplification effects on crash severity across different driver groups. These findings may support the design of evidence-based interventions to mitigate crash severity among ride-hailing drivers.

HIGHLIGHTS

• The study explored risk factors affecting crash severity for ride-hailing drivers.

• The similarities and differences between part-time and full-time ride-hailing drivers were analyzed.

• Individual and combined effects of risk factors were identified and compared for the two types of drivers.

• Valuable suggestions were provided according to the characteristics of different types of drivers.

KEYWORDS:

• Ride-hailing drivers
• crash risk factors
• employment status
• Bayesian network
• safety promotion

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China Youth Program [52302443]; Shaanxi Provincial Department of Education Special Research Program Project [23JK0126].