Objective: The rapid development of information technologies and the COVID-19 pandemic have resulted in the proliferation of online food shopping and food delivery motorcyclists. In contrast to the relatively ample literature on factors influencing fatalities and risky riding behaviors of food delivery motorcyclists, little is known about the determinants of non-fatal crashes involving online food delivery riders. The present study examines the prevalence and factors of non-fatal crashes among food delivery riders.
Methods: The self-reported data of 393 online food delivery riders were collected in Hanoi and Hochiminh city, Vietnam. Binary logit regression was used to investigate the factors associated with non-fatal crashes.
Results: The findings showed that more than half of riders (54%) reported being involved in at least one crash in the last 12 months. The most common risky riding behaviors associated with the crashes included using a mobile phone while riding, neglecting turn signals, red-light running, riding when tired/sleepy, and speeding. The riders who were national migrants, married, and worked on planned delivery routes mainly alone were more likely to experience crashes. At the same time, adequate perceived rewards for their work prevent crash involvement. Perceived risk was not a significant predictor of self-reported crashes.
Conclusions: Ensuring road safety for delivery riders requires a systemic effort involving multiple stakeholders, and the private sector plays a crucial role in discouraging risky riding behaviors. It is imperative for the government and regulatory bodies to redefine the delivery job to alleviate the strain on riders and provide resources such as rewards. Specifically, riders should be considered employees rather than partners. Furthermore, it is crucial for the police to take a more active role in preventing dangerous behaviors among delivery riders, such as running red lights. At the same time, supporting financial strategies should be implemented for delivery riders, particularly for those who are migrants or married and may face additional challenges.
Keywords: Accident; binary logistic regression; food delivery; non-fatal crashes; risky riding behavior; workload.