Analysis of SHRP2 Speeding Data

Richard, Christian M.; Lee, Joonbum; Brown, James L.; Landgraf, Andrew · 2020 · ROSA P / United States. Department of Transportation. National Highway Traffic Safety Administration

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Summary

This study addresses the persistent issue of speeding-related crashes in the United States, which accounted for 26% of fatal crashes in 2017. Despite various countermeasures, speed-related fatalities have not substantially decreased since 2000. The research aims to improve the understanding of speeding behavior to inform the development of targeted countermeasures by examining situational and driver-specific predictors, categorizing types of speeding, and developing a typology of speeders. The researchers analyzed data from the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study, which collected data from 3,539 drivers across six U.S. sites between 2010 and 2013. The final sample included 2,910 drivers aged 16–65 and 203,831 trips. The study defined "Free-Flow Episodes" (FFEs) as periods where drivers had the opportunity to speed and "Speeding Episodes" (SEs) as continuous periods where vehicle speed exceeded the posted speed limit (PSL) by at least 10 mph for more than 6 seconds. For primary analysis, SEs were further filtered to include only those reaching at least 15 mph above the PSL, resulting in 71,113 episodes. The researchers employed descriptive statistics, linear regression models to identify predictors of speeding within FFEs and at the driver level, and k-means cluster analysis to categorize speeding types and driver groups. The findings revealed that speeding was relatively uncommon, with only 13% of drivers spending more than 5% of their FFE time speeding. Significant predictors of speeding included situational factors such as longer FFE duration, driving during morning, afternoon, or evening hours, and driving in spring or fall months. Demographically, less experienced drivers (typically younger) sped more. Driver-specific factors strongly correlated with speeding included self-reported traffic violations, crash history, higher sensation-seeking scores, and aggressive or risk-taking driving behaviors. Additionally, a higher ADHD confidence index was associated with increased speeding. The cluster analysis identified three distinct types of speeding: "Momentary" (brief, transient speed increases), "Cruising" (longer duration, low variability, speed maintenance), and "Riskier" (active speed control with higher variability). The study concludes that speeding is influenced by a complex interplay of situational opportunities, driver demographics, and psychological traits, particularly risk affinity. By identifying distinct types of speeding and driver groups, the research provides a foundation for developing more effective, targeted countermeasures. The identification of specific driver profiles, such as those with high sensation-seeking or ADHD indicators, suggests that interventions should be tailored to address the specific behavioral and attitudinal drivers of speeding rather than applying a one-size-fits-all approach.

Key finding

Only 13% of drivers spent more than 5% of their free-flow driving time speeding, and speeding behavior was significantly predicted by lower driving experience, higher sensation-seeking, and prior traffic violations or crashes.

Methodology

naturalistic

Sample size: 2910

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
• sex gender
• urban rural setting
• incidence prevalence
• cultural cross national
• sensation seeking

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