Community-based, macro-level collision prediction models

The burden on communities due to the enormous economic and social costs associated with road collisions has been recognized worldwide as a major problem of epidemic proportions. Given the magnitude and persistence of the problem, spanning many decades, organizations worldwide have initiated engineering and research programs to improve road safety. There are two main transportation engineering approaches to improving the safety performance of the road component: reactive and proactive. The reactive or traditional engineering approach has been to address road safety in reaction to existing collision histories. While it has proven to be very successful, road safety authorities and researchers are also pursuing more proactive engineering approaches. Rather than working reactively to improve the safety of existing facilities, the proactive engineering approach to road safety improvement focuses on predicting and improving the safety of planned facilities. Reactive and proactive programs both rely heavily on reliable empirical techniques, including collision prediction models (CPMs). Reactive programs use micro-level collision prediction models, which focus on a single facility. Reliable micro-level C PM methods and techniques have been well researched and refined. However, while micro-level CPMs successfully support the reactive engineering approach, several shortcomings have been identified related to unsuccessful attempts by planners and engineers to use them in proactive road safety planning. Given these shortcomings of micro-level CPMs in planning-level (i.e. macro-level) road safety evaluations, there exists a research gap of reliable empirical tools to pursue road safety in a proactive manner. In view of this lack of reliable macro-level empirical tools, the main goal of this thesis was to develop macro-level CPMs, and to provide guidelines for their use by planners and engineers, so that road safety could be explicitly considered and reliably estimated in all stages of the road planning process. The approach taken included developing macro-level CPMs using extensive data extraction and Generalized Linear Regression Modeling (GLM) regression techniques, and then developing guidelines for use of those models based on several case studies of road safety planning applications. This thesis describes the results of that research on the development and use of community-based, macro-level CPMs using data from 577 neighbourhoods or Traffic Analysis Zones (TAZs) across the Greater Vancouver Regional District in British Columbia, Canada. The models predict mean collision frequency based on associations with variables from one of four neighbourhood characteristic themes, including exposure, socio-demographics (S-D), Transportation Demand Management (TDM), and network. A set of model use guidelines has also been proposed. To test whether the developed models and guidelines could be practical and relevant for practitioners, this research has also demonstrated the use of macro-level CPMs in several reactive and proactive case studies. The development of models and model-use guidelines in this research, together with their application in several case studies, have been offered as contributions toward addressing the research gap that has limited the effectiveness of the proactive engineering approach in road safety improvement programs. It is believed that these tools will contribute significantly to improved safety planning decisions by community planners and engineers, significantly enhanced effectiveness in road safety improvement programs, and, ultimately, to long term social and economic benefits for all communities.

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