A framework for analyzing collisions, near misses and injuries of commercial cyclists

Abstract

Jobs using bicycles have diversified beyond bicycle messengering and seen a recent surge, especially with respect to the delivery of prepared food and the provision of services such as moving companies. Freight companies are also increasingly using cargo bicycles for last mile logistics. Yet little is known on the risks of injuries and collisions for commercial cyclists.Using 36 interviews of commercial cyclists, employers, contractors and entrepreneurs in the Montréal metropolitan area, Canada, we develop a framework for occupational safety factors related to risks of near collisions, collisions and injuries for different types of cyclist workers.The framework presents 21 factors organized in 4 large categories that may be associated with safety and health issues: individual characteristics and traits (experience, risk-taking behavior), work type and working conditions, exposure levels, and external factors (not related to workers or companies). Workers have different backgrounds and working conditions, use different bicycle types and other equipment and are accordingly potentially exposed to distinct and varying levels of risk. Many injuries are reported. Personal health issues including repetitive stress injuries, mental exertion and food intake emerged from interviews as a potentially risk-enhancing feature.Commission-based work performed in crowded business districts during peak traffic periods are likely the three highest road-related injury risk given exposure levels. Experience of cyclists and the promotion of safe practices by employers are key protective factors, but pay structure may be the single most important features to improve the safety of workers. Because using bicycles as work tools is likely to grow over time, it is important to understand how city planning, work organization, industry regulation, and education of workers and employers may reduce risks to commercial cyclists on the roads. The framework can serve future inferential studies.