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Watershed Planning within a Quantitative Scenario Analysis Framework
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Applying machine learning, text mining, and spatial analysis techniques to develop a highway-railroad grade crossing

Samira Soleimani1, Michael Leitner1, Julius Codjoe2

  • 1Geography &Anthropology Department, Louisiana State University, Baton Rouge, LA, 70802, United States.

Accident; Analysis and Prevention
|January 25, 2021
PubMed
Summary

Highway-railroad grade crossing consolidation reduces crashes. A new model using machine learning, text mining, and geospatial analysis achieved 88% accuracy, identifying 15% of crossings for closure to improve safety.

Keywords:
Highway-rail grade crossing consolidationMachine learningSpatial analysisText mining

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Area of Science:

  • Transportation Safety
  • Machine Learning Applications
  • Geospatial Analysis

Background:

  • Highway-railroad grade crossing (HRGC) consolidation is key to reducing train-vehicle accidents.
  • East Baton Rouge Parish experienced 57 HRGC crashes from 2015-2019, causing injuries and significant vehicle damage costs.
  • Identifying optimal crossings for closure in consolidation programs presents a challenge.

Purpose of the Study:

  • To develop a localized HRGC consolidation model for improved safety.
  • To enhance previous machine learning models with text mining and geospatial analysis.
  • To identify specific highway-rail grade crossings suitable for closure.

Main Methods:

  • Utilized eXtreme Gradient Boosting (XGboost) machine learning algorithm.
  • Integrated Text Mining Techniques and Geospatial Analysis with XGboost.
  • Performed sensitivity analysis on aggregation gain and correlation thresholds.

Main Results:

  • Achieved an overall model accuracy of 88%.
  • Identified key variables influencing HRGC prediction and model behavior.
  • Developed a simplified model with 14 variables, 95% aggregated gain, and 0.5 correlation threshold.

Conclusions:

  • The developed model effectively identifies highway-rail grade crossings for closure.
  • Recommended closure of 15% of current highway-rail grade crossings based on the model.
  • The findings offer a data-driven approach to enhance transportation safety through HRGC consolidation.