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Suttida Changsan 1 Article
A decision tree model for traffic accident prediction among food delivery riders
Muslimah Molo, Suttida Changsan, Lila Madares, Ruchirada Changkwanyeun, Supang Wattanasoei, Supa Vittaporn, Patcharin Khamnuan, Surangrat Pongpan, Kasama Pooseesod, Sayambhu Saita
Epidemiol Health. 2024;e2024095.   Published online November 26, 2024
DOI: https://doi.org/10.4178/epih.e2024095    [Accepted]
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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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