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Related Experiment Video

Updated: Jun 10, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Identifying crash-prone locations with quantile regression.

Xiao Qin1, Marie Ng, Perla E Reyes

  • 1Department of Civil and Environmental Engineering, South Dakota State University, CEH 148, Box 2219, Brookings, SD 57007, United States. xiao.qin@sdstate.edu

Accident; Analysis and Prevention
|August 24, 2010
PubMed
Summary
This summary is machine-generated.

Identifying high-risk road locations is crucial for safety. Quantile regression offers a more refined method for pinpointing problem areas compared to traditional approaches.

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Last Updated: Jun 10, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Transportation Engineering
  • Statistical Modeling
  • Road Safety Analysis

Background:

  • Limited budgets necessitate efficient identification of safety improvement locations.
  • Current crash modeling often focuses on mean-level changes, which is insufficient for skewed and heterogeneous crash data.
  • Existing methods for identifying risk-prone intersections may not fully capture the nuances of safety data.

Purpose of the Study:

  • To introduce and evaluate quantile regression for identifying intersections with severe safety issues.
  • To compare the effectiveness of quantile regression against classic approaches for risk-prone intersection identification.
  • To demonstrate the utility of quantile regression in handling heterogeneous crash data.

Main Methods:

  • Application of quantile regression technique to analyze crash data.
  • Estimation of trends at different quantiles to capture data heterogeneity.
  • Comparison with several classic approaches for determining risk-prone intersections.

Main Results:

  • Quantile regression provides a more flexible estimation of trends across different data quantiles.
  • The method effectively addresses the heterogeneity often present in crash data.
  • Quantile regression identified a more refined and sensible subset of risk-prone locations compared to other methods.

Conclusions:

  • Quantile regression is a valuable tool for identifying locations with severe safety issues.
  • This method offers superior refinement in pinpointing risk-prone intersections, especially with heterogeneous data.
  • The findings support the adoption of quantile regression for more effective road safety management and resource allocation.