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A novel approach for analyzing severe crash patterns on multilane highways.

Anurag Pande1, Mohamed Abdel-Aty

  • 1Department of Civil & Environmental Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, United States. apande@calpoly.edu

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This study introduces a new method to analyze severe highway crashes on multilane roads. It identifies key factors contributing to different crash types, aiding in future safety improvements.

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Area of Science:

  • Transportation Engineering
  • Traffic Safety Analysis
  • Roadway Design

Background:

  • Severe crashes on multilane highways pose significant safety risks.
  • Existing analysis methods often aggregate data or are affected by under-reporting of minor incidents.
  • Understanding crash patterns on mid-block segments with partial access is crucial for targeted interventions.

Purpose of the Study:

  • To present a novel analytical approach for severe crashes on multilane highway mid-block segments.
  • To identify significant contributing factors for distinct severe crash types (rear-end, lane-change, pedestrian, single-vehicle).
  • To develop a framework for safety evaluation using general roadway characteristics.

Main Methods:

  • A within-stratum matched case-control approach comparing severe crashes to non-crash instances.
  • Utilizing random sampling of time, day, and location (milepost) for matched strata.
  • Deriving geometric design, roadside, and traffic characteristics from milepost data.
  • Analyzing four crash groups (rear-end, lane-change, pedestrian, single-vehicle) separately against non-crash cases.

Main Results:

  • Severe lane-change crashes are linked to exposure; single-vehicle and pedestrian crashes show no significant relation to Average Daily Traffic (ADT).
  • Significant factors for severe rear-end crashes include speed limit, ADT, K-factor, time, median type, pavement condition, and horizontal curvature.
  • The approach effectively uses general roadway characteristics, avoiding reliance on event-specific details.

Conclusions:

  • The novel approach provides a robust method for analyzing severe crashes without data aggregation or bias from minor crash under-reporting.
  • Specific roadway and traffic factors are identified as critical for different severe crash types on multilane arterials.
  • This methodology offers a valuable tool for enhancing arterial segment safety evaluations.