Identification and Prioritization of Hazardous Road Locations by Segmentation and Data Envelopment Analysis Approach

Sadeghi, Aliasghar; Ayati, Esmaeel; Neghab, Mohammadali Pirayesh · 1970 · Crossref

DOI: 10.7307/ptt.v25i2.1295

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Summary

This study addresses the challenge of identifying and prioritizing hazardous road locations, specifically accident-prone sections (APSs), to facilitate effective safety management and resource allocation. The authors argue that traditional methods, such as regression models or simple accident frequency counts, are limited by distributional assumptions or stochastic variability. To overcome these limitations, the paper proposes a novel approach combining road segmentation with Data Envelopment Analysis (DEA). This method evaluates the relative efficiency of road sections by considering multiple inputs (geometric and environmental factors) and outputs (accident frequency and severity), allowing for a more comprehensive assessment of risk that accounts for the interaction of various causal factors. The methodology was applied to a case study involving 144.4 km of two-way, two-lane rural roads in Khorasan Razavi, Iran. The researchers first segmented the roads into 154 homogeneous sections based on changes in key geometric and traffic characteristics, including average annual daily traffic, lane width, speed limits, curvature change rate, and pavement condition. Data collection involved GPS surveys for alignment, field inspections for roadside hazards and access density, and historical accident records from 2004–2005. The DEA model utilized these geometric and environmental features as inputs and a weighted accident index as the output. The accident index combined frequency and severity, assigning weights of 1, 3, and 5 to property damage, injury, and fatal accidents, respectively. The study employed the CCR model for efficiency measurement and the Andersen-Petersen method to rank efficient units. The results identified 11 accident-prone sections among the 154 analyzed segments. These sections were prioritized based on their inefficiency values derived from the DEA model. The findings demonstrated that prioritization based solely on accident frequency and severity is insufficient. Instead, the DEA approach provided a more robust rating by evaluating the ratio of accidents to the combination of influencing factors. The method successfully distinguished between efficient and inefficient road sections, offering a clear ranking for decision-making. The significance of this research lies in its provision of a systematic, non-parametric tool for road safety management. By using DEA, the approach avoids the statistical assumptions required by regression models and considers multiple inputs and outputs simultaneously. This allows transportation agencies to identify hazardous locations more accurately and prioritize engineering countermeasures effectively. The authors conclude that this method can be broadly applied to prioritize intersections, roundabouts, or entire road networks, thereby optimizing the use of limited safety resources.

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• Empirical Findings: crash risk outcomes