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A data mining approach to deriving safety policy implications for taxi drivers
Jiwon Park1, Seolyoung Lee2, Cheol Oh3
1Department of Smart City Engineering, Hanyang University at Ansan, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-city, Gyeonggi-do 15588, Republic of Korea.
Journal of Safety Research
|March 3, 2021
Summary
Taxi driver characteristics significantly impact traffic safety, with higher crash rates than other vehicles. This study identifies high-risk drivers and proposes policies to improve taxi safety and driver well-being.
Area of Science:
- Transportation Science
- Occupational Health
- Data Mining
Background:
- Taxi crashes occur more frequently than with other vehicle types, highlighting significant traffic safety concerns.
- Understanding the intrinsic characteristics of taxi drivers is crucial for developing effective safety policies.
Purpose of the Study:
- To analyze the intrinsic characteristics of taxi drivers and their relationship to traffic accidents.
- To derive policy implications for enhancing taxi traffic safety and improving driver welfare.
Main Methods:
- A questionnaire survey collected data from 781 corporate taxi drivers in Korea.
- Two-stage data mining, including random forest, classified drivers into four types based on working conditions and welfare.
Main Results:
- Four driver types were identified: unspecified normal, work-life balanced, overstressed, and work-oriented.
- Factors contributing to high-risk taxi drivers were prioritized for policy development.
Conclusions:
- Policy recommendations include new measures (elderly driver evaluation, mental health programs), improvements (wage system, rest facilities), and elimination of negative factors (overtime restrictions).
- Implementing these policies is expected to reduce taxi accidents and enhance driver quality of life.
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