Comparison of SHRP 2 Naturalistic Driving Data to Geometric Design Speed Characteristics on Freeway Ramps

Brewer, Marcus A.; Stibbe, Jayson · 2020 · ROSA P / University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP)

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Summary

This study addresses the need to update geometric design guidelines for freeway entrance and exit ramps, which currently rely on practices dating back several decades. Existing research often suffers from limited field data, small sample sizes, and reliance on spot speeds rather than comprehensive speed profiles. The authors utilized the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) dataset to analyze detailed driving behavior and compare it against ramp design characteristics. The primary objective was to develop models for calculating operating speeds at key locations along freeway ramps and to evaluate how well current design guidelines reflect actual driver behavior. The researchers analyzed data from 100 selected ramps across six states, comprising 10,895 unique participant-ramp combinations and nearly 1.4 million recorded trips. They extracted high-frequency time series data (recorded every 0.1 second) including vehicle speed, acceleration, and yaw rate. Because the SHRP 2 Roadway Information Database lacked sufficient geometric data for the selected sites, the team used Google Earth to manually measure ramp characteristics, such as curve radius and segment length. A significant methodological challenge was locating vehicles precisely on the ramps using time series data; the authors developed a technique using yaw rate and a "threshold of turning" to align vehicle positions with specific ramp segments (curves vs. tangents). They prioritized geometric design variables over others to create intuitive speed prediction models using SAS software. The study produced separate speed prediction models for curved segments of entrance and exit ramps. For entrance ramp curves, the model incorporated the freeway speed limit, curve radius (and its square), and traffic control type at the crossroad terminal. For exit ramp curves, the model included the freeway speed limit, radius, the percentage of the ramp traversed, and traffic control indicators. The analysis confirmed that with such a large dataset, many variables were statistically significant, necessitating a focus on geometric and traffic control factors to produce meaningful results. The authors also compared their findings to existing models from NCHRP Project 3-105 and the Highway Safety Manual (HSM), noting that previous HSM-based models tended to overestimate speeds on loop ramps. The significance of this work lies in providing a robust, data-driven foundation for updating freeway ramp design guidelines. By leveraging the extensive SHRP 2 NDS dataset, the study offers a more accurate representation of free-flow operating speeds than previous limited-field studies. The resulting models allow designers to better predict vehicle speeds based on geometric characteristics, potentially improving safety and operational efficiency. The study also identifies limitations in current data collection methods and suggests topics for future research, such as refining the estimation of superelevation and further validating speed profiles on tangent segments.

Key finding

The study developed new regression models for predicting operating speeds on freeway ramps using naturalistic driving data, which were subsequently compared to existing prediction models from recent research.

Methodology

naturalistic

Sample size: 10895

Provenance

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

Topics

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• speed choice
• road geometry
• perceptual countermeasures

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