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A model to identify high crash road segments with the dynamic segmentation method.

Amin Mirza Boroujerdian1, Mahmoud Saffarzadeh1, Hassan Yousefi2

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Accident; Analysis and Prevention
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This study introduces a new method to identify and measure high-risk road segments, crucial for prioritizing safety improvements. The approach accurately pinpoints crash locations and segment lengths, enhancing road safety strategies.

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Area of Science:

  • Transportation Engineering
  • Road Safety Analysis
  • Signal Processing

Background:

  • Road safety is a critical issue due to significant social, economic, physical, and mental costs.
  • Existing road segmentation models have deficiencies, particularly in accurately identifying the length of high-crash road segments.
  • Budget and applicability limitations necessitate precise identification of high-risk road segments for effective safety improvements.

Purpose of the Study:

  • To present a novel approach for identifying both the location and length of high-crash road segments.
  • To address the limitations of current road segmentation models in determining the precise extent of hazardous road sections.
  • To enable effective prioritization of road safety improvements by accurately defining high-risk areas.

Main Methods:

  • Developed a dynamic model based on wavelet theory to convert accident data into a road response signal.
  • Utilized multi-scale segmentation to identify high-crash road segments of varying lengths, including smaller segments within larger ones.
  • Evaluated the model's performance by applying it to a real-world case study.

Main Results:

  • The novel approach successfully identified high-crash road segments and their lengths.
  • The multi-scale segmentation capability allowed for the detection of crash segments at different scales.
  • Application to a real case identified 10-20% of high-crash road segments with a 25-38% improvement over existing methods.

Conclusions:

  • The presented wavelet-based dynamic model offers a significant advancement in identifying high-crash road segments and their lengths.
  • The multi-scale segmentation feature provides a more comprehensive understanding of road safety issues.
  • This method enhances the ability to prioritize road safety investments more effectively.