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Crashes at Highway–Rail Grade Crossings (HRGCs) that involve a truck or a train carrying hazardous materials (hazmat) expose people and the environment to potentially severe consequences of hazmat release. This research involved statistical modeling of the probability of hazmat release from trucks and/or trains in crashes at HRGCs to identify factors associated with hazmat release. The Federal Railroad Administration (FRA) HRGC crash dataset (2007–2016) yielded two subsets of crashes: 1) those involving hazmat-carrying trucks, and 2) those involving hazmat-carrying trains. Results from a logistic regression model using data subset 1 (crashes involving hazmat-carrying trucks) with hazmat release/no release as the response variable showed that standard flashing signal lights, railroad crossbucks, and railroad classes II and III (relative to railroad class I) were associated with lower hazmat release probability from hazmat-carrying trucks. Hazmat release probability from trucks was higher with freight train involvement. Results from a logistic regression model using data subset 2 (crashes involving hazmat-carrying trains) revealed that hazmat release probability from trains was lower with warmer temperature. However, the probability of release from trains was greater with railroad class II (relative to railroad class I), type of highway user (different types of trucks and motorcycle relative to automobiles), and weather conditions (fog, sleet or snow, relative to clear). A comparison of the results from this study with HRGC crash severity studies highlighted the importance and usefulness of this study.

In this study, response surface methodology was utilized for creating a continuous response function and optimization of effective variables to improve the tensile strength ratio, as an indicator of stripping potential, using factorial experiment with center composite design. The polynomial regression model equations were proposed based on the regression coefficients calculated for the indirect tensile strength in dry and saturated condition as well as tensile strength ratio. Statistical analyses indicated that all first and second orders plus interactive terms were statistically significant with 90 % confidence level except for interactive terms of bitumen-Sasobit content in tensile strength ratio, and grading-Sasobit content in dry indirect tensile strength. Furthermore, it was found that all first, second and interactive terms were statistically significant for saturated indirect tensile strength.