Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study

Abstract

The ordered probit model was the primary technique, and CART and association rules act as the supporter and identifier of interactions between variables. All 3 algorithms' results consistently show that the most influential accident factors are train speed, VRU age, and gender. The findings of this research could be applied for identifying high-risk hotspots and developing cost-effective countermeasures targeting VRUs at HRGCs.

Deaths on rail roads: a study from Colombo North Teaching Hospital, Sri Lanka

“…Although poor illumination may be a contributory factor but it is not a major determinant according to this study. 8 In contrast a German study revealed marked seasonal, weekly and diurnal peaks of railway suicide intensity. Differences between men and women indicate sexspecific processes underlying their suicidal behaviour.…”

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

“…Ghomi et al [23] applied an ordered probit model, association rules, and classification and regression tree (CART) algorithms to the US Federal Railroad Administration's (FRA) HRGC accident database for the period 2007-2013 to identify VRU injury severity factors at HRGCs. Using six years of nationwide crashes from 2009 to 2014 in the US, Haleem [24] applied both the mixed logit and binary logit models based on the multiple predictors investigation (e.g., temporal crash characteristics, geometry, railroad, traffic, vehicle, and environment).…”

A Simultaneous Safety Analysis of Crash Frequency and Severity for Highway-Rail Grade Crossings: The Competing Risks Method

“…Most of the previous research studies have focused on roadway intersection or roadway crashes [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Relatively little research effort has focused on HRGC accidents compared to roadway accidents [15][16][17][18][19][20][21][22][23][24]. Moreover, among all the previous HRGC accident analyses, the majority of them focus only either on crash frequency, often based on crossing inventory databases [19,[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], or on crash severity analysis, often based on historical crash police report databases [16,[40][41][42][43][44][45][46]…”

“…Relatively little research effort has focused on HRGC accidents compared to roadway accidents [15][16][17][18][19][20][21][22][23][24]. Moreover, among all the previous HRGC accident analyses, the majority of them focus only either on crash frequency, often based on crossing inventory databases [19,[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], or on crash severity analysis, often based on historical crash police report databases [16,[40][41][42][43][44][45][46][47][48][49][50][51][52]. To understand and predict crash frequency and severity simultaneously and consistently is important for agencies seeking to improve safety so they can account for the common factors affecting both crash frequency and severity.…”