PW 2152 Improving cyclist safety: understanding the relationship between road infrastructure and passing distance

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IntroductionCycling is an alternative mode of transport to motor vehicles that has numerous health and economic benefits. However, in countries with limited infrastructure to separate cyclists from motorised traffic, cyclists must regularly share the road with motor vehicles. Often driver pass cyclists in close proximity, potentially increasing collision risk. Close passing events can also heighten subjective risk and create a barrier to cycling participation.MethodsAn on-road observational study was conducted in Victoria, Australia. Volunteer participants, recruited using a convenience sample, recorded all cycling trips over a one to two-week period. Participants’ bicycles were fitted with a purpose-built and independently calibrated device to measure lateral passing distance of all motor vehicles, a video camera and a GPS datalogger. For each passing event, road infrastructure (presence of marked on-road bicycle lane, intersection-related, etc), and vehicle type were classified. Multi-level mixed-effects regression was used to investigate the effect of these factors on passing distance.ResultsSixty-three participants recorded 18 246 passing events. Of these, 6% (n=1,084) were close passing events (less than one metre). Results from the mixed-effects model demonstrated that factors associated with reduced passing distance included: the vehicle type being a taxi, four-wheel drive, or bus, relative to a sedan; passing events occurring in intersections, relative to mid-block; and the presence of a marked bicycle lane, relative to no bicycle lane. Specifically, compared to roads with no marked bicycle lane and no parked cars, passing events in which the cyclist was riding in a marked bicycle lane next to parked cars resulted in passing distances that were 0.41 m closer.ConclusionThese findings demonstrate that road infrastructure and design can have significant effects on passing distances. Globally, these data can be used to inform the selection and design of cycling-related infrastructure with the aim of improving safety for cyclists.

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