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Pedestrian and Bicyclist Safety at Highway-Rail Grade Crossings (Year 1 Report)
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2024-09-15
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[PDF-4.30 MB]
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Edition:June 1, 2023 – August 31, 2024
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Abstract:Published literature is sparse on non-motorist (pedestrians and bicyclists) crashes at Highway-rail Grade Crossings (HRGCs), despite their involvement in rail-related crashes. A critical aspect of crash prediction models for HRGs is crash exposure, which measures activities of interest at specific locations. While data on motor vehicle and train traffic at HRGCs are available from different sources, non-motorist traffic counts are not readily available. Current Federal Railroad Administration (FRA) models focus on vehicular crash exposure, overlooking non-motorized traffic; gathering non-motorized traffic data is crucial for improving crash prediction models. In this study, non-motorist traffic videos were recorded for 1,848 hours from various urban and suburban HRGCs in Nebraska followed by application of the AI-based You Only Look Once Version 8 (YOLOv8) algorithm for automated non-motorist volume detection. Additionally, data on grade crossing characteristics, including population density and land use, were collected to create a comprehensive non-motorist database. Statistical and AI models were developed to analyze non-motorist exposure in terms of daily traffic volumes, utilizing physical, dynamic, and temporal characteristics of HRGCs. The models indicated that sidewalks, improved visibility, and cloudy weather conditions were associated with increased non-motorist traffic volume. Conversely, higher motor vehicle traffic levels, adverse weather conditions (rain and snow), industrial zones, and greater number of traffic lanes were linked with lower non-motorist traffic. This foundational study aims to enhance crash prediction models at HRGCs by incorporating non-motorist traffic factors, potentially improving crash prediction models and non-motorist safety at HRGCs.
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