A decision tree model for traffic accident prediction among food delivery riders

• 발행기관: 한국역학회
• 저자명: Muslimah Molo Suttida Changsan Lila Madares Ruchirada Changkwanyeun Supang Wattanasoei Supa Vittaporn Patcharin Khamnuan Surangrat Pongpan Kasama Pooseesod Sayambhu Saita
• 간행물 정보: 『Epidemiology and Health』46, 1~11쪽, 전체 11쪽
• 주제분류: 의약학 > 면역학
• 파일형태: PDF
• 발행일자: 2024.01.31

국문 초록

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.

목차

INTRODUCTION

MATERIALS AND METHODS

RESULTS

DISCUSSION

NOTES

REFERENCES

키워드

Food delivery riders Traffic accidents Decision tree model Prediction Mitigation