Advancing the science of road safety management through innovation in crash and non-crash based analysis

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According to the Canadian Council of Motor Transportation Administrators (CCMTA), each year in Canada, almost 2,000 people are killed and 165,000 are injured (10,000 seriously) while using Canada's roads, costing society $37 billion. The CCMTA website maintains a user searchable inventory of road safety initiatives which road safety stakeholders can adopt or adapt to address their specific road safety challenges. Although some of these have been “proven” effective, it is noted that for others, measured effectiveness is not yet available, and these are thus considered only as “promising”. There is a need for knowledge on measured safety effectiveness in order to reclassify those promising measures, particularly infrastructure related ones, to “proven”, and in the process to develop crash modification factors that are vital for estimating safety benefits in cost-benefit analysis and project prioritization in planning infrastructure safety measures. In fulfilling this need, there are limits to what can be achieved with traditional road safety analytical methods, and research returns in this area are rapidly diminishing despite the dearth of knowledge. To advance the science in addressing this void, I propose a five-year research plan with a long-term goal of filling elusive knowledge gaps on the safety implications of road infrastructure decisions. Some of these knowledge gaps are likely to occur in the future as traffic flow on roads is increasingly impacted by the various levels and mixtures of automation that now seem inevitable in the not so distant future. The five-year program will have three inter-related components that form the short-term objectives. The first component will continue my existing research on developing crash modification functions that relate the safety impacts of an engineering decision to application circumstances using crash and roadway data. This knowledge will not only allow more precise estimation of safety effects in making road infrastructure decisions in planning interventions, but will also facilitate the transferability of the derived knowledge. The second component will utilize the extensive naturalistic driving data recently acquired in the United States and Canada to investigate the viability of using crash surrogates to derive knowledge on safety effects of road design characteristics. The third component aims to quantify the safety performance of roads with mixes of manual and automated vehicles, recognizing that full automation and full penetration may be in the distant future. The proposed research will not only benefit the road safety research community through advancing the science in safety analysis, but it will also contribute towards fulfilling the objectives of the Canada's Road Safety Strategy (RSS) 2025 recently developed by the CCMTA in helping to achieve the Strategy's goal of making Canada's roads the safest in the world.