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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Developing safety performance functions incorporating reliability-based risk measures.

Shewkar El-Bassiouni Ibrahim1, Tarek Sayed1

  • 1Dept. of Civil Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4.

Accident; Analysis and Prevention
|August 9, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a reliability-based risk measure, the probability of non-compliance (P(nc)), into road safety performance functions (SPFs). Incorporating P(nc) significantly improved the accuracy of collision prediction models for horizontal curves.

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Area of Science:

  • Transportation Engineering
  • Road Safety Analysis
  • Probabilistic Design

Background:

  • Current geometric design standards lack explicit safety margins and risk assessment for deviations.
  • Probabilistic geometric design offers a way to quantify uncertainty and risk but lacks integration with collision frequency.

Purpose of the Study:

  • To bridge the gap between reliability measures and safety quantification by linking probability of non-compliance (P(nc)) with safety performance functions (SPFs).
  • To enable the inclusion of reliability-based design in benefit-cost analyses for wider road design application.

Main Methods:

  • Developed reliability-based quantitative risk measures, specifically P(nc), for integration into SPFs.
  • Applied First Order Reliability Method (FORM) for reliability analysis.
  • Utilized a comprehensive database of two-lane rural highway collisions and geometric design.
  • Developed two Negative Binomial (NB) SPFs to compare models with and without P(nc).

Main Results:

  • Models incorporating P(nc) demonstrated a superior fit to the collision data compared to traditional NB SPFs.
  • This improved fit was observed for total collisions, injury and fatality (I+F) collisions, and property damage only (PDO) collisions.

Conclusions:

  • The integration of reliability-based risk measures like P(nc) enhances the predictive accuracy of safety performance functions.
  • This approach facilitates a more robust and risk-informed decision-making process in road geometric design, particularly for horizontal curves.