Leveraging the Second Strategic Highway Research Program Naturalistic Driving Study: Examining Driver Behavior When Entering Rural High-Speed Intersections

Jackson, Steven · 2017 · ROSA P / United States. Federal Highway Administration. Office of Safety Research and Development

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Summary

This study investigates driver stopping and scanning behaviors when approaching rural high-speed, stop-controlled intersections, leveraging data from the second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS). The research was motivated by the high prevalence of crashes at these locations; 40% of U.S. crashes occur at intersections, with 57% of fatalities from 1997–2004 occurring at stop-controlled intersections, 61% of which were in rural areas. Contributing factors include inadequate surveillance, failure to yield, inattention, and speed. The primary objective was to quantify brake and glance patterns to produce actionable safety insights, while secondary goals included assessing the utility of SHRP2 data for further safety questions. The analysis utilized naturalistic driving data from 31 unique drivers who completed 411 crossings through four homogeneous rural intersections in Pennsylvania. These intersections featured major routes with posted speed limits exceeding 50 mi/h and stop-controlled minor routes. Data extraction involved static variables (demographics, average annual mileage, self-reported risk perception) and time-series variables (speed, acceleration, brake usage, GPS coordinates) recorded at 10 Hz. Video data were reduced by trained staff to code eyeglance locations across ten regions of interest and traffic presence. Statistical models, including generalized estimating equations, were employed to analyze brake distance, the probability of making a complete stop, and glance duration across five 98.4-ft segments of the approach. Key findings indicate that brake distance was sufficiently predicted by brake speed. At an average brake speed of 61.7 mi/h, drivers applied brakes at an average distance of 328.7 ft from the intersection. Older drivers (ages 45–84) braked farther upstream than younger drivers (ages 18–44), particularly at higher speeds. The probability of making a complete stop varied significantly with average annual mileage (AAM) and self-reported risk perception; drivers with higher AAM were more likely to make complete stops. Visual scanning analysis revealed that drivers spent nearly the entire approach glancing forward until 98.4 ft from the intersection. Between 0 and 98.4 ft, drivers spent an average of 5.1 seconds scanning the intersecting roadway, with 86.5% of all intersection scanning occurring in this final segment. A distinct behavioral difference emerged based on stop type: drivers making complete stops spent only 39.2% of pre-stop time scanning the intersection, whereas those performing rolling stops spent 74.5%. This suggests complete stoppers focus on arrival before scanning, while rolling stoppers scan prior to arrival to maintain speed while perceiving safety. The study concludes that SHRP2 NDS data provide valuable, ecologically valid insights into intersection behavior, highlighting distinct strategies between complete and rolling stoppers. The findings underscore the influence of age, mileage, and risk perception on stopping behavior and demonstrate that the majority of critical visual scanning occurs in the final moments before intersection entry. These results offer actionable data for transportation agencies to improve infrastructure and target safety resources effectively.

Key finding

Drivers who performed complete stops spent 39.2 percent of prestop time scanning the intersection, whereas rolling stoppers spent 74.5 percent of that time scanning.

Methodology

naturalistic

Sample size: 31

Provenance

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

Topics

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• rail grade crossings
• temporal
• eye movements scanning
• driver vru interaction
• attention allocation
• looked but failed to see

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: behavioral performance data, observational prevalence
• Methodological Resource: dataset resource