Development of pedestrian- and vehicle-related safety performance functions using Bayesian bivariate hierarchical models with mode-specific covariates |
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Institution: | 1. Department of Civil Engineering, California State Polytechnic University, Pomona, CA 91768, United States;2. Traffic Safety Investigations Branch, Department of Transportation California, United States;3. Division of Research, Innovation and System Information, Department of Transportation California, United States;4. Department of Logistics Engineering, Logistics and Traffic College, Central South University of Forestry and Technology, Hunan 410004, China |
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Abstract: | Introduction: Pedestrian safety is a major concern as traffic crashes are the leading cause of fatalities and injuries for commuters. Traffic safety research in the past has developed various strategies to counteract traffic crashes, including the safety performance function (SPF). However, there is still a need for research dedicated to enhancing the SPF for pedestrians from perspectives of methodological framework and data input. To fill this gap, this study aims to add to the current SPF development practice literature by focusing on pedestrian-involved collisions, while considering the typical vehicle ones as well. Methods: First, bivariate models are used to account for the common unobserved heterogeneity shared by the pedestrian- and vehicle-related crashes at the same intersections. Second, variable importance ranking technique is used, along with correlation analysis, to determine mode-specific feature input. Third, the exposure information for both modes, annual pedestrian count, and annual daily vehicles traveled are used for model development. Fourth, a recent Bayesian inference approach (integrated nested Laplace approximation (INLA)) was adopted for bivariate setting. Finally, different evaluation criteria are used to facilitate comprehensive model assessment. Results: The results reveal different statistically significant factors contributing to each of the modes. The offset intersection provides better safety performance for both pedestrians and drivers as compared to other intersection designs. The model findings also corroborate the sensibility of using the bivariate models, rather than the separate univariate ones. Practical Applications: The study shows that pedestrians are more vulnerable to various intersection features such as left-turn channelization, intersection control, urban and rural population group, presence of signal mastarm on the cross-street, and mainline average daily traffic. Greater focus should be directed toward such intersection features to improve pedestrian safety. |
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Keywords: | Pedestrian-Vehicles crashes Crash frequency Models Bivariate models Pedestrian count Safety Performance Function |
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