Using SHRP2-Nds Data to Investigate Freeway Operations, Human Factors, and Safety: Final Report

Avelar, Raul Eduardo; Dadashova, Bahar; Higgins, Laura; Gillette, George · 2018 · ROSA P / ATLAS Center (Mich.)

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Summary

This study investigates the interrelationships between traffic operations, human factors, and safety outcomes using data from the Second Strategic Highway Research Project (SHRP2). The research addresses the gap in understanding how microscopic operational variables (e.g., speed, gap) and driver characteristics (e.g., age, demographics) influence safety outcomes (crashes or near-crashes). Traditional research often isolates these domains, but this project leverages the SHRP2 Naturalistic Driving Study (NDS) and Roadway Information Database (RID) to quantify these interactions across multiple temporal scales. The researchers conducted two distinct studies. Study 1 analyzed nearly 800 freeway driving events from Washington and Florida under uncongested, non-curve conditions. Using dynamic mixed-effects models, the team examined Speed of Choice (SOC) in free-flow situations and car-following behavior. Study 2 focused on freeway ramps in Pennsylvania, utilizing over 2,000 trip records from more than 60 drivers. This study employed time-series reduction, clustering, and neural networks to identify predictors of speed choice and speeding behavior on ramps. In Study 1, results indicated that while driver characteristics such as age, experience, and vision conditions correlated with SOC, facility type, traffic density, and posted speed limits were the most influential factors for free-flow driving. In car-following scenarios, estimated reaction times increased significantly with age, rising from 1.1 seconds for drivers under 20 to 2.2 seconds for those over 69. Younger drivers exhibited higher sensitivity to relative speed and following gaps, making larger speed adjustments than other groups. Drivers aged 70 and older adjusted speeds similarly to those aged 20–39 but with delayed reactions. All age groups except younger drivers adjusted car-following speeds toward their predicted free-flow SOC. Study 2 found that traffic conditions and geographic location were the primary predictors of speed choice on ramps, leading to more uniform speed adjustments among drivers. Driver characteristics influenced SOC to a lesser degree. Notably, drivers exhibiting higher variability in speed were associated with higher Barkley Attention Deficit Hyperactivity Disorder scores, suggesting a link between distractibility and inconsistent speed control. The findings imply that human factors, particularly age and attention, significantly impact operational behaviors like reaction time and speed consistency. These insights can improve microsimulation models for traffic engineering and inform road design to accommodate diverse driver populations, especially as the demographic shifts toward older drivers. The study highlights the utility of naturalistic data in bridging operational metrics with safety outcomes.

Key finding

Driver reaction time in car-following situations increases with age, rising from 1.1 seconds for drivers under 20 to 2.2 seconds for drivers over 69, while posted speed limits remain the primary predictor of free-flow speed choice.

Methodology

naturalistic

Sample size: 800

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.

• speed choice
• following distance
• traffic density
• naturalistic crash near crash
• sex gender
• temporal

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