Safety Performance Functions for Low-Volume Roads

Dell’Acqua, Gianluca; Russo, Francesca · 2011 · OpenAlex-citations

DOI: 10.3846/bjrbe.2011.29

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study addresses the need for accurate safety performance functions for low-volume rural roads, which constitute a significant portion of the roadway network but often lack specific crash prediction models. The research focuses on two-lane rural roads in the Province of Salerno, Italy, characterized by traffic volumes under 1,000 vehicles per day (vpd). The primary objective was to calibrate injurious crash prediction models (CPMs) per kilometer per year for two distinct terrain types: flat/rolling areas with vertical grades less than 6% and mountainous areas with vertical grades greater than 6%. This work is part of a larger, long-term research program initiated in 2003 to improve safety performance on Southern Italian rural roads. The methodology utilized a network approach, analyzing 983.58 km of roadway divided into homogeneous segments based on curvature change rates. The dataset comprised crash records from 2003 to 2005, covering 63 segments in flat/rolling terrain and 151 segments in mountainous terrain. Variables included average daily traffic (ADT), mean speed, roadway width, curvature indicators, and vertical grade indicators. The authors employed non-linear multi-variable regression analysis using the Gauss-Newton method and ordinary least squares to estimate model coefficients. Model validity was assessed using adjusted coefficients of determination, p-values, and cumulative residual analysis to ensure statistical significance and goodness-of-fit. The results yielded two distinct injurious crash prediction models. For flat/rolling terrain, the model achieved an adjusted coefficient of determination ($r^2$) of 91%, with ADT showing minimal influence on crash frequency, consistent with low-volume conditions. For mountainous terrain, the model demonstrated a higher fit with an $r^2$ of 99%. In both models, variables such as mean speed, curvature, vertical grade, and roadway width were statistically significant predictors of injurious crashes. The study found that the average injury count over the three-year period was 0.94 crashes per km for flat/rolling roads and 0.32 crashes per km for mountainous roads. The structural form of the mountainous model aligned closely with established rural highway models from North America. The significance of this research lies in providing calibrated tools for estimating expected crash frequencies on low-volume roads, which are often underserved by standard safety models. These CPMs enable transportation agencies to identify high-risk "black spot" segments and prioritize safety improvement projects based on quantitative evidence. By linking crash outcomes to specific geometric and environmental features, the models support data-driven decision-making for infrastructure maintenance and design consistency evaluations, ultimately aiming to reduce injury rates on rural networks.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• incidence prevalence

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: crash risk outcomes