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Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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Examining driver injury severity in left-turn crashes using hierarchical ordered probit models.

Zhao Zhang1, Runan Yang2, Yun Yuan1

  • 1Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah.

Traffic Injury Prevention
|November 18, 2020
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Summary

This study analyzed left-turn crashes, identifying factors that increase or decrease driver injury severity. Winter conditions significantly alter these contributing factors, highlighting the need for seasonal safety considerations.

Keywords:
Left-turn crashhierarchical ordered probit modelinjury severity

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Area of Science:

  • Traffic Safety
  • Accident Analysis
  • Injury Prevention

Background:

  • Left-turn crashes are a significant concern in traffic safety.
  • Existing research has limited focus on the specific contributing factors and injury severity of left-turn crashes.
  • Understanding these factors is crucial for developing targeted safety interventions.

Purpose of the Study:

  • To comprehensively investigate the contributing factors of left-turn crashes.
  • To analyze the corresponding driver injury severity in these crashes.
  • To identify seasonal variations in contributing factors, particularly distinguishing between winter and other seasons.

Main Methods:

  • Utilized a hierarchical ordered probit (HOPIT) model to analyze driver injury severity in left-turn crashes.
  • Segmented the crash dataset into "winter" and "other-season" subsets based on environmental conditions.
  • Examined contributing factors influencing injury severity within each seasonal subset.

Main Results:

  • In non-winter seasons, factors like young/male drivers and clear conditions decreased severity, while alcohol, disregard for traffic devices, and high-speed limits increased it.
  • In winter, alcohol, disregard for traffic devices, and high-speed limits increased severity, whereas two-vehicle involvement and snow decreased it.
  • Eighteen significant factors were identified overall, with winter exhibiting fewer significant factors and distinct patterns.

Conclusions:

  • The HOPIT model effectively identified key factors influencing left-turn crash injury severity.
  • Seasonal variations, especially winter conditions, significantly impact the contributing factors and severity of left-turn crashes.
  • Findings underscore the importance of considering seasonal context in traffic safety strategies for left-turn crashes.