New insights into active transportation safety : a macro-level analysis framework

City councils worldwide have shown an increasing interest in active transportation (AT) due to its health, environmental, and economical benefits. However, active commuters are vulnerable to severe crash risk, which is a deterrent to active travel. Therefore, there is a need for developing systematic approaches to improve AT safety. This dissertation introduces a comprehensive framework for identifying, diagnosing and remedying the macro-level AT safety issues. It provides original insights into AT networks, crash models (CM), crash hot zones identification (HZID), and policy recommendations. Data were collected from 134 traffic analysis zones (TAZs) in the City of Vancouver. Cyclist and pedestrian crash data, traffic exposure and large GIS data were incorporated in the analysis. The GIS data integrated various land use, built environment, socioeconomic, and road facility features. Moreover, bike and pedestrian network indicators, developed using graph-theory and representing connectivity, continuity, and topography of the networks, were incorporated. The state of the practice empirical Bayesian (EB) method and the state of the art full Bayesian (FB) methods were adopted for the CMs’ development and HZID. Various FB model forms were investigated, and the Spatial Poisson-Lognormal model performed the best. Cyclist and pedestrian crashes were found positively associated with various attributes of network-connectivity, socio-demographics, built environment, arterial-collector roads, and commercial areas. Conversely, the crashes were negatively associated with various attributes of network-directness, network-topography, residential areas, recreational areas, local roads, separated paths, and actuated signals. Most of the safety correlates had similar effects for the pedestrian and cyclist crashes. Accordingly, mixed multi-response FB CMs were developed and the correlation between pedestrian and cyclist crashes was found significant. The univariate/multivariate CMs with spatial effects consistently outperformed those without, and the multivariate CMs generally outperformed the univariate ones. AT crash hot-zones were then identified using the novel Mahalanobis distance and the conventional potential for safety improvement (PSI) methods, and consistency tests were applied to compare both. Afterwards, trigger variables were statistically identified for the crash hot and safe zones. Lastly, remedies regarding land use, traffic demand, and traffic supply management were proposed based on the trigger variables’ analysis, field studies, and literature consultation.Applied Science, Faculty ofCivil Engineering, Department ofGraduat