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Original article A decision tree model for traffic accident prediction among food delivery riders
Muslimah Moloorcid , Suttida Changsanorcid , Lila Madaresorcid , Ruchirada Changkwanyeunorcid , Supang Wattanasoeiorcid , Supa Vittapornorcid , Patcharin Khamnuanorcid , Surangrat Pongpanorcid , Kasama Pooseesodorcid , Sayambhu Saitaorcid
Epidemiol Health 2024;e2024095
DOI: https://doi.org/10.4178/epih.e2024095 [Accepted]
Published online: November 26, 2024
Thammasat University, Lampang, Thailand
Corresponding author:  Sayambhu Saita,
Email: sayambhu.s@fph.tu.ac.th
Received: 15 September 2024   • Revised: 3 November 2024   • Accepted: 14 November 2024
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Abstract

OBJECTIVES
Food delivery riders (FDRs) play a crucial role in the food delivery industry but face considerable challenges, including a rising number of traffic accidents. This study aimed to examine the incidence of traffic accidents and develop a decision tree model to predict the likelihood of traffic accidents among FDRs.
METHODS
A cross-sectional study was conducted with 257 FDRs in Chiang Mai and Lampang Province, Thailand. Participants were interviewed using questionnaires and provided self-reports of accidents over the previous 6 months. Univariable logistic regression was used to identify factors influencing traffic accidents. Subsequently, a decision tree model was developed to predict traffic accidents using a training and validation dataset split in a 70:30 ratio.
RESULTS
The results indicated that 45.14% of FDRs had been involved in a traffic accident. The decision tree model identified several significant predictors of traffic accidents, including delivering food in the rain, job stress, fatigue, inadequate sleep, and the use of a modified motorcycle, achieving a prediction accuracy of 66.54%.
CONCLUSIONS
Based on this model, we recommend several measures to minimize accidents among FDRs: ensuring adequate sleep, implementing work-rest schedules to mitigate fatigue, managing job-related stress effectively, inspecting motorcycle conditions before use, and exercising increased caution when delivering food during rainy conditions.

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