Reference number : GC-2017-Q1-02561, amendment number : , agreement type : G, recipient legal name : Sacchi, Emanuele (University of Saskatchewan), recipient country : CA, recipient province : nan, recipient city : Saskatchewan, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : The way transportation engineers study and evaluate road and traffic safety has remarkably advanced in recent years. Engineers can now employ a systematic and rigorous process to analyze safety at road sites that identifies opportunities to reduce crashes and implement potential safety countermeasures. This process mainly relies on a set of statistical techniques and models developed over several years of research. In turn, these models rely on crash observations as the main data source to quantify safety levels. However, the use of crash data in safety models has several limitations, mainly related to quality and availability issues. Moreover, the use of crash observations is reactive: a substantial number of crashes must occur before any action is taken. These limitations also inhibit application of new opportunities offered by methodological advancements in the field. 
Surrogate safety data represents a promising alternative to the exclusive use of crashes in road safety analysis. So far, limited research has been carried out to estimate and validate coefficients converting surrogate measures to crash frequency. Nevertheless, the continual advance in statistical methodologies has enabled researchers to extract more and more information from existing data sources. For this reason, the proposed research program is aimed at implementing advanced methodologies in road safety using non-crash-based safety measures and establishing a functional relationship between crashes and crash surrogates. 
The short-term objectives of the proposed research program are to: (1) collect and measure emerging surrogate safety data and indicators to overcome the limitations related to crash data; (2) validate the use of surrogate safety data obtained from traffic microsimulation models by establishing a relationship with field-measured surrogates; and (3) establish a functional relationship between crashes and crash surrogates by taking advantage of the new methodological frontier in road safety statistics. The results of this research are expected to enhance safety performance of highways by increasing the level of knowledge associated with the safety implications of traffic operations and road design choices. This will have direct impact on the sustainability of our society due to the enormous economic and human costs related to road casualties., program purpose french : The way transportation engineers study and evaluate road and traffic safety has remarkably advanced in recent years. Engineers can now employ a systematic and rigorous process to analyze safety at road sites that identifies opportunities to reduce crashes and implement potential safety countermeasures. This process mainly relies on a set of statistical techniques and models developed over several years of research. In turn, these models rely on crash observations as the main data source to quantify safety levels. However, the use of crash data in safety models has several limitations, mainly related to quality and availability issues. Moreover, the use of crash observations is reactive: a substantial number of crashes must occur before any action is taken. These limitations also inhibit application of new opportunities offered by methodological advancements in the field. 
Surrogate safety data represents a promising alternative to the exclusive use of crashes in road safety analysis. So far, limited research has been carried out to estimate and validate coefficients converting surrogate measures to crash frequency. Nevertheless, the continual advance in statistical methodologies has enabled researchers to extract more and more information from existing data sources. For this reason, the proposed research program is aimed at implementing advanced methodologies in road safety using non-crash-based safety measures and establishing a functional relationship between crashes and crash surrogates. 
The short-term objectives of the proposed research program are to: (1) collect and measure emerging surrogate safety data and indicators to overcome the limitations related to crash data; (2) validate the use of surrogate safety data obtained from traffic microsimulation models by establishing a relationship with field-measured surrogates; and (3) establish a functional relationship between crashes and crash surrogates by taking advantage of the new methodological frontier in road safety statistics. The results of this research are expected to enhance safety performance of highways by increasing the level of knowledge associated with the safety implications of traffic operations and road design choices. This will have direct impact on the sustainability of our society due to the enormous economic and human costs related to road casualties., agreement title english : Advancing knowledge of the safety implications of transportation engineering decisions with surrogate safety data and innovative statistical techniques, agreement title french : Advancing knowledge of the safety implications of transportation engineering decisions with surrogate safety data and innovative statistical techniques, agreement number : RGPIN, agreement value : 130000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada