Investigating Nonmotorist Crash Exposure at Highway–Rail Grade Crossings Using Artificial Intelligence-Based Object Detection and Generalized Linear Count Models

Abstract

A critical aspect of crash prediction models for highway–rail grade crossings (HRGCs) is crash exposure, which is a measure of train and highway traffic. Although data on motor vehicle traffic (e.g., annual average daily traffic) and train traffic at HRGCs are invariably available, nonmotorist traffic data at HRGCs are not readily available. Current Federal Railroad Administration and other HRGC crash models focus on train and motor vehicle traffic, overlooking nonmotorized traffic. Therefore, there is a need to gather nonmotorized traffic data to improve HRGC crash prediction models. To address this gap, nonmotorist traffic video data were recorded in this study at various urban and suburban HRGCs in Nebraska, followed by the application of an artificial intelligence-based You Only Look Once (version 8) algorithm for automated nonmotorist traffic volume detection. Data on HRGC characteristics, including surrounding area population density and land use, were collected to create a comprehensive HRGC safety database for nonmotorists. Three negative binomial models were estimated to analyze pedestrian, bicyclist, and combined nonmotorist exposure in relation to daily volumes, utilizing physical, dynamic, and temporal characteristics of HRGCs. Results indicated that sidewalks, greater visibility, and cloudy weather conditions were associated with increased nonmotorist traffic volume. Conversely, higher vehicular traffic levels, wet road conditions, low population density, and more traffic lanes correlated with lower nonmotorist traffic. This study established an initial framework for nonmotorist traffic monitoring and identified key environmental and technical challenges in automated detection at HRGCs; based on these findings, recommendations for addressing technical limitations were provided for future research.