Hybrid Machine Learning and Geographic Information Systems Approach — A Case for Grade Crossing Crash Data Analysis

Abstract

Highway-rail grade crossing (HRGC) accidents continue to be a major source of transportation casualties in the United States. This can be attributed to increased road and rail operations and/or lack of adequate safety programs based on comprehensive HRGC accidents analysis amidst other reasons. The focus of this study is to predict HRGC accidents in a given rail network based on a machine learning analysis of a similar network with cognate attributes. This study is an improvement on past studies that either attempt to predict accidents in a given HRGC or spatially analyze HRGC accidents for a particular rail line. In this study, a case for a hybrid machine learning and geographic information systems (GIS) approach is presented in a large rail network. The study involves collection and wrangling of relevant data from various sources; exploratory analysis, and supervised machine learning (classification and regression) of HRGC data from 2008 to 2017 in California. The models developed from this analysis were used to make binary predictions [98.9% accuracy & 0.9838 Receiver Operating Characteristic (ROC) score] and quantitative estimations of HRGC casualties in a similar network over the next 10 years. While results are spatially presented in GIS, this novel hybrid application of machine learning and GIS in HRGC accidents’ analysis will help stakeholders to pro-actively engage with casualties through addressing major accident causes as identified in this study. This paper is concluded with a Systems-Action-Management (SAM) approach based on text analysis of HRGC accident risk reports from Federal Railroad Administration.