Improving Railroad Grade Crossing Safety: Accident Prediction Models Using Macro- and Micro-Scale Analysis
Rahim Benekohal, Professor, N/A
This project is working to 1) develop a methodology for analyzing rail crossing crashes at a micro level to discover trends at a single crossing or a series of crossings along a corridor or a region; and 2) improve the accuracy of crash predictions by incorporating the findings from microscopic analyses and by studying the regional trends that emerge in this analysis but are not observed at a national level. This new micro approach has resulted in identification and valuation of the contribution of new variables not previously considered. Researchers found that the distance to the nearest highway-highway intersection is an important factor in gated-crossing crash prediction. They also found that the angle between the railroad and the highway is an important factor in crash prediction for crossings with flashing lights. Improvements in predicting crashes results in a more reliable ranking and selection of crossings for safety improvements as well as more accurate cost-benefit estimations, optimizing resource allocation procedures, and, therefore, reducing crash frequencies to a greater extent than using current practices. The addition of information resulting from the microanalysis into macro models is expected to further enhance predictions and to provide a multi-scale perspective not previously studied in this context. To complement dynamic tree analysis to find contributing factors to crash frequency, "Crossing Cluster" is calculated for each crossing. Crossing Cluster represents the contribution of a variable to crash frequency at that crossing, which often is larger than the contribution found in the dynamic tree.