Epidemiology and Health

Original Articles

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

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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 45.1% 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

Quantitative descriptive analysis and electronic nose combined with machine learning methods to quickly predict consumer preference for Cheddar cheese
Ying Wang, Xinying Xu, Yadong Wang, Jian He, Ziyan Wu, Sharuna De, Xiaoyan Pei, Houxi Leng, Bei Wang
Journal of Dairy Science.2026; 109(2): 1160. CrossRef
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

Type 2 diabetes mellitus increases the severity of non-fatal injuries, but not the risk of fatal injuries, among driver victims of motor vehicle crashes in Taiwan: I-Lin Hsu, Wen-Hsuan Hou, Ya-Hui Chang, Chung-Yi Li; Epidemiol Health. 2022;44:e2022076. Published online September 16, 2022; DOI: https://doi.org/10.4178/epih.e2022076

13,842 View
136 Download
2 Web of Science
2 Crossref

Abstract Summary PDF Supplementary Material

Abstract

OBJECTIVES
Limited information is available on whether diabetes increases the severity of injuries from motor vehicle crashes (MVCs). This study aimed to investigate the association of type 2 diabetes with injury severity among driver victims of MVCs.

METHODS

This cohort study involved 75,737 adult driver victims with type 2 diabetes from Taiwan’s Police-Reported Traffic Accident Registry in 2015-2017, along with 150,911 sex-, age-, and calendar year-matched controls. The severity level of non- fatal injuries was derived from the International Classification of Diseases Programs for Injury Categorization based on the diagnostic codes of National Health Insurance claims within 3 days after an MVC. Information on fatal injuries within 3 days after an MVC was obtained from the Taiwan Death Registry. Logistic regression models were used to estimate the odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) of injury severity in association with type 2 diabetes.

RESULTS

After adjusting for potential confounders, driver victims with type 2 diabetes experienced significantly higher risks of mild and severe non-fatal injuries than their counterparts without diabetes, with covariate-adjusted ORs of 1.08 (95% CI, 1.05 to 1.11) and 1.28 (95% CI, 1.20 to 1.37), respectively. By contrast, the adjusted OR for fatal injuries was not significantly elevated, at 1.02 (95% CI, 0.89 to 1.18). Similar results were found when car and scooter driver victims were analyzed separately.

CONCLUSIONS

Type 2 diabetes was found to moderately increase the severity of non-fatal injuries from MVCs among car and scooter driver victims.

Summary

Key Message
With 75,737 driver victims with diabetes and 150,911 matched controls, this study showed an 8% and 28% increase in mild and severe non-fatal injury, respectively among driver victims with diabetes. Such increase in risk was equally applied to both car and scooter drivers. No increase in risk of 3-day mortality after crash was found.

Citations

Citations to this article as recorded by

Prevalence of diabetes mellitus among traumatic patients admitted to Aseer Central Hospital, Aseer Region, Abha, Saudi Arabia: cross-sectional study
Majed Mohammed Al Saleh, Bandar A. Alasmari, Ali Mohammed AlAmri, Mohammed Mabkhoot Mogbel, Ali Saeed Alasmary, Alhussein Ali Almonawar, Saeed Doos S. Almontashri, Hassan Mussa Al Mojamad, Turki Abdullah Al Qahtani, Abdulrahman Mohammed Alshehri, Ibrahim
BMC Endocrine Disorders.2025;[Epub] CrossRef
A population-based study on meteorological conditions in association with motor vehicle collisions among people with type 2 diabetes
Chung-Yi Li, Ya-Hui Chang, Hon-Ping Ma, Ping-Ling Chen, Chang-Ta Chiu, I-Lin Hsu
Environmental Health and Preventive Medicine.2025; 30: 91. CrossRef

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