Molo Muslimah, Changsan Suttida, Madares Lila, Changkwanyeun Ruchirada, Wattanasoei Supang, Vittaporn Supa, Khamnuan Patcharin, Pongpan Surangrat, Pooseesod Kasama, Saita Sayambhu
Faculty of Public Health, Thammasat University, Lampang, Thailand.
Thammasat University Research Unit in Environment, Health and Epidemiology, Pathum Thani, Thailand.
Epidemiol Health. 2024;46:e2024095. doi: 10.4178/epih.e2024095. Epub 2024 Nov 26.
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.
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.
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%.
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.
食品配送骑手在食品配送行业中发挥着至关重要的作用，但面临着诸多挑战，包括交通事故数量不断上升。本研究旨在调查交通事故的发生率，并开发一个决策树模型来预测食品配送骑手发生交通事故的可能性。
对泰国清迈和南邦府的257名食品配送骑手进行了一项横断面研究。通过问卷调查对参与者进行访谈，并让他们提供过去6个月内的事故自我报告。采用单变量逻辑回归来确定影响交通事故的因素。随后，使用按70:30比例划分的训练和验证数据集开发了一个决策树模型来预测交通事故。
结果表明，45.1%的食品配送骑手曾发生过交通事故。决策树模型确定了几个交通事故的重要预测因素，包括在雨中送餐、工作压力、疲劳、睡眠不足以及使用改装摩托车，预测准确率达到66.5%。
基于该模型，我们建议采取多项措施以尽量减少食品配送骑手的事故：确保充足睡眠、实施工作休息时间表以减轻疲劳、有效管理与工作相关的压力、使用前检查摩托车状况，以及在雨天送餐时格外小心。
