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