Evaluating Curve Speed Behavior Using Shrp 2 Data

Geedipally, Srinivas R.; Pratt, Michael P.; Dadashova, Bahar; Wu, Lingtao; Shirazi, Mohammadali · 2017 · ROSA P / ATLAS Center (Mich.)

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Summary

This study addresses the safety concerns associated with horizontal curves, which are linked to a disproportionate number of severe crashes, particularly single-vehicle run-off-road and head-on collisions. The research was motivated by the need to validate existing speed prediction models using comprehensive naturalistic driving data and to understand how driver familiarity and curve severity influence crash rates. Specifically, the authors aimed to assess the transferability of speed prediction models developed in Texas to Indiana, identify relationships between driver familiarity and operating speeds, and simultaneously evaluate curve severity and crash rates. The researchers utilized data from the Second Strategic Highway Research Program (SHRP 2), integrating the Naturalistic Driving Study (NDS) database with the Roadway Information Database (RID) and historical crash records. The analysis focused on 252 curves across four rural roadway sections in Indiana. The methodology involved matching GPS speed data from NDS trips with geometric data from RID using coordinate tolerances and heading calculations. The study analyzed operating speeds at the point of curvature, midpoint, and point of tangency. Additionally, the authors assessed curve severity using side friction differential and correlated these metrics with eight years of crash data. The results demonstrated that speed differential models developed in Texas could be extended to Indiana by applying a multiplicative adjustment factor, confirming their transferability. The study found that familiar drivers exhibited higher 85th-percentile speeds at the curve midpoint compared to unfamiliar drivers. However, the speed prediction models performed more accurately for unfamiliar drivers than for familiar ones. Regarding safety outcomes, the analysis revealed that for curves categorized as more severe, the side friction differential was positively associated with crash rates. This indicates that curves requiring greater lateral acceleration relative to the approach tangent are linked to higher frequencies of crashes. The significance of this research lies in its validation of cross-state applicability for speed prediction models, suggesting that agencies can adapt existing tools with minor calibration rather than developing new models from scratch. The findings on driver familiarity highlight distinct behavioral patterns that affect speed consistency, implying that advisory speed settings may need to account for local driver knowledge. Furthermore, the positive association between side friction differential and crash rates supports the use of this metric as a robust measure of curve severity for safety assessments and countermeasure prioritization.

Key finding

Texas-developed speed prediction models are transferable to Indiana with a multiplicative adjustment factor, familiar drivers travel faster than unfamiliar drivers at curve midpoints, and side friction differential is positively associated with crash rates for severe curves.

Methodology

naturalistic

Sample size: 252

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. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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

• road geometry
• speed choice
• 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, observational prevalence
• Methodological Resource: dataset resource