A New Accident Prediction Model for Highway-Rail Grade Crossings Using the USDOT Formula Variables

This paper presents the ZINDOT model,a methodology utilizing a zero-inflated negative binomial model with the variables used in the United States Department of Transportation(USDOT)accident prediction formula,to determine the expected accident count at a highway-rail grade crossing.The model developed contains separate formulas to estimate the crash prediction value depending on the warning device type installed at the crossing:crossings with gates,crossings with flashing lights and no gates,and crossings with crossbucks.The proposed methodology also accounts for the observed accident count at a crossing using the Empirical Bayes method.The ZINDOT model estimates were compared to the USDOT model estimates to rank the crossings based on the expected accident frequency.It is observed that the new model can identify crossings with a greater number of accidents with Gates and Flashing Lights and Crossbucks in both Illinois(data which were used to develop the model)and Texas(data which were used to validate the model).A practitioner already using the USDOT formulae to estimate expected accident count at a crossing could easily use the ZINDOT model as it employs the same variables used in the USDOT formula.This methodology could be used to rank highway-rail grade crossings for resource allocation and safety improvement.

Effective LiDAR Damage Detection:Comparing Two Detection Algorithms

The health conditions of highway bridges is critical for sustained transportation operations.US federal government mandates that all bridges built with public funds are to be inspected visually every two years. There is a growing consensus that additional rapid and non-intrusive methods for bridge damage evaluation are needed.This paper explores the potential of applying ground-based laser scanners for bridge damage evaluation. LiDAR has the potential of providing high-density,full-field surface static imaging.Hence,it can generate volumetric quantification of concrete corrosion or steel erosion.By recording object surface topology,LiDAR can detect different damages on the bridge structure and differentiate damage types according to the surface flatness and smoothness.To determine the effectiveness of LiDAR damage detection,two damage detection algorithms are presented and compared using scans on actual bridge damages.The results demonstrate and validate LiDAR damage quantification,which can be a powerful tool for bridge condition evaluation.