Accident Prediction Models using Macro and Micro Scale Analysis: Dynamic Tree and Zero Inflated Negative Binomial Models with Empirical Bayes Accident History Adjustment

This report presents two ways to analyze accidents at highway rail grade crossings: a microscopic approach that looks at individual accidents at a crossing or a group of crossings, and a macroscopic approach to identify correlations between accident counts at crossings and crossing characteristics. The outcome of the microscopic approach is a data-driven dynamic tree that helps to visualize accident trends at a single crossing or a group of crossings. The dynamic tree is also used to identify new variables (crossing angle and distance to nearby highway intersection). The outcome of the macroscopic approach were new accident prediction models for crossings with gates, flashing lights, and crossbucks. Zero Inflated Negative Binomial models were used to predict the accident counts and the Empirical Bayes approach was used to adjust the predicted based on accident history at the crossing. Data from the state of Illinois was used to develop the model and data from four other states were used to validate the model. The newly developed models resulted in cumulative predicted accident distributions that closely represent the field data. The EB adjusted ZINB accident predictions value were significantly closer to the actual accident counts for the crossings than the USDOT models. More accurate predictions from the EB-adjusted ZINB model were obtained for the top 10, 20, 30, 40 and 50 locations with highest accident frequency for all three warning devices.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; Photos; References; Tables;
  • Pagination: 102p

Subject/Index Terms

Filing Info

  • Accession Number: 01698780
  • Record Type: Publication
  • Report/Paper Numbers: CTS 19-02, CTS#2015056
  • Contract Numbers: DTRT13-G-UTC35
  • Files: UTC, TRIS, ATRI, USDOT
  • Created Date: Feb 26 2019 2:07PM