Investigation of Design Speed Characteristics on Freeway Ramps using SHRP2 Naturalistic Driving Data

Brewer, Marcus A.; Stibbe, Jayson · 2019 · ROSA P / SAGE Publications

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Summary

This study investigates the relationship between freeway ramp design characteristics and actual driver operating speeds to evaluate the relevance of existing geometric design guidelines. Current design standards, primarily derived from the AASHTO *Green Book*, have remained largely unchanged for decades. The authors argue that these guidelines require review in the context of modern driving behaviors and vehicle capabilities. Previous research efforts were limited by small sample sizes or incomplete speed profiles, often relying on spot-speed measurements or instrumented vehicles with few participants. To address these limitations, this research utilizes the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) dataset, which provides unprecedented detail and volume of naturalistic driving data. The methodology involved identifying 1,686 ramps from the SHRP2 NDS database across six states, which were filtered down to a candidate list of 100 ramps with robust trip data. The final dataset comprised 10,895 unique participant-ramp combinations, yielding over 1.7 million individual speed readings recorded at 0.1-second intervals. Since the SHRP2 Roadway Information Database lacked sufficient alignment data for ramps, the researchers used Google Earth to manually measure geometric characteristics, including curve radii and segment lengths. The data were analyzed using SAS to develop regression models predicting vehicle speeds on curved and tangent ramp segments. Variables included radius, freeway speed limits, traffic control types at terminals, and the percentage of the ramp traversed. The results yielded distinct models for entrance and exit ramps. For curved segments, the models explained 45.4% and 50.5% of the variance in speed for entrance and exit ramps, respectively. Key findings indicated that freeway speed limits significantly influenced entrance ramp speeds, while crossroad traffic control had a greater impact on exit ramp speeds. For tangent segments, the models demonstrated higher explanatory power, with coefficients of determination of 0.761 for entrance ramps and 0.794 for exit ramps. These models revealed that vehicles accelerate by an average of 8 mph on entrance tangents and decelerate by 11.5 mph on exit tangents. Additionally, the presence of adjacent curved segments affected speed adjustments on tangents, with drivers slowing down on entrance tangents if preceded or followed by curves. The significance of this work lies in its potential to update freeway ramp design guidance. By leveraging large-scale naturalistic data, the study provides empirical evidence on how drivers actually respond to ramp geometry, contrasting with theoretical design assumptions. The developed models offer a more accurate estimation of operating speeds than previous methods, such as those in the Highway Safety Manual, which were found to overestimate speeds on loop ramps. These findings support the need for revised design criteria that better reflect current driver behavior, potentially improving safety and operational efficiency on freeway interchanges. The study serves as a foundation for future research aimed at refining geometric design standards.

Key finding

Operating speeds on curved ramp segments are primarily determined by curve radius and freeway speed limit, while tangent segment speeds are strongly predicted by the speed at the segment's start and the type of adjacent segments.

Methodology

naturalistic

Sample size: 10895

Provenance

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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.

• speed choice
• road geometry
• perceptual countermeasures
• speed management

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