Skip Navigation
Skip to contents

Epidemiol Health : Epidemiology and Health

OPEN ACCESS
SEARCH
Search
Advanced Search
mobile menu button

Search

Page Path
HOME > Search
1 "Muslimah Molo"
Filter
Filter
Article category
Keywords
Publication year
Authors
More +
Funded articles
Original Article
A decision tree model for traffic accident prediction among food delivery riders in Thailand
Muslimah Molo, Suttida Changsan, Lila Madares, Ruchirada Changkwanyeun, Supang Wattanasoei, Supa Vittaporn, Patcharin Khamnuan, Surangrat Pongpan, Kasama Pooseesod, Sayambhu Saita
Epidemiol Health. 2024;46:e2024095.   Published online November 26, 2024
DOI: https://doi.org/10.4178/epih.e2024095
  • 4,154 View
  • 136 Download
  • 1 Web of Science
  • 2 Crossref
AbstractAbstract PDF
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 45.1% 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 66.5%.
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.
Summary

Citations

Citations to this article as recorded by  
  • Modeling Riding and Stopping Behaviors at Motorcycle Box Intersections: A Case Study in Chiang Mai City, Thailand
    Wachira Wichitphongsa, Nopadon Kronprasert, Moe Sandi Zaw, Pongthep Pisetsit, Thaned Satiennam
    Infrastructures.2025; 10(4): 97.     CrossRef
  • The Impact of Conservative Estimated Time of Arrival Algorithm on Food Delivery Rider Behaviors
    Miao Yu, Hailiang Chen, Yuan Cheng, Yu Jeffrey Hu
    SSRN Electronic Journal.2023;[Epub]     CrossRef
Epidemiol Health : Epidemiology and Health
© Korean Society of Epidemiology.
TOP