An empirical tool to evaluate the safety of cyclists

Today, North American governments are more willing to consider compact neighborhoods with increaseduse of sustainable transportation modes. Bicycling, one of the most effective modes for short trips withdistances less than 5 km is being encouraged. However, as vulnerable road users (VRUs), cyclists aremore likely to be injured when involved in collisions. In order to create a safe road environment forthem, evaluating cyclists’ road safety at a macro level in a proactive way is necessary. In this paper,different generalized linear regression methods for collision prediction model (CPM) development arereviewed and previous studies on micro-level and macro-level bicycle-related CPMs are summarized. Onthe basis of insights gained in the exploration stage, this paper also reports on efforts to develop negativebinomial models for bicycle–auto collisions at a community-based, macro-level. Data came from theCentral Okanagan Regional District (CORD), of British Columbia, Canada. The model results revealedtwo types of statistical associations between collisions and each explanatory variable: (1) An increasein bicycle–auto collisions is associated with an increase in total lane kilometers (TLKM), bicycle lanekilometers (BLKM), bus stops (BS), traffic signals (SIG), intersection density (INTD), and arterial–localintersection percentage (IALP). (2) A decrease in bicycle collisions was found to be associated with anincrease in the number of drive commuters (DRIVE), and in the percentage of drive commuters (DRP).These results support our hypothesis that in North America, with its current low levels of bicycle use (<4%),we can initially expect to see an increase in bicycle collisions as cycle mode share increases. However,as bicycle mode share increases beyond some unknown ‘critical’ level, our hypothesis also predicts a netsafety improvement. To test this hypothesis and to further explore the statistical relationships betweenbicycle mode split and overall road safety, future research needs to pursue further development andapplication of community-based, macro-level CPMs.