Motivations for Speeding [Traffic Tech]

NHTSA · 2012 · ROSA P / United States. Department of Transportation. National Highway Traffic Safety Administration

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Summary

This report by the National Highway Traffic Safety Administration (NHTSA) addresses the motivations behind speeding, a behavior estimated to contribute to one-third of U.S. traffic fatalities. While previous research identified factors associated with speeding, the relative importance of these factors remained unclear. To clarify this, NHTSA conducted a naturalistic driving study aimed at identifying why drivers speed, classifying speeders into distinct groups, and examining how situational, demographic, and personality factors predict travel speed. The study utilized GPS data collected from 164 volunteers in Seattle, Washington, and College Station, Texas, over three to four weeks. Participants were divided equally into four demographic groups: young males (18–25), older males (35–55), young females (18–25), and older females (35–55). In addition to GPS tracking, participants completed personal inventories measuring demographics, personality traits, attitudes, and risk-taking behaviors. Speeding was operationally defined as driving 10 mph or more above the posted speed limit during free-flow conditions. By analyzing the percentage of trips involving speeding and the average amount of speeding per trip, researchers identified four distinct speeding patterns: Incidental (rare, low-intensity speeding), Situational (rare but high-intensity speeding, often due to being late), Regular or Casual (frequent but low-intensity speeding), and Habitual (frequent, high-intensity speeding). The findings revealed that speeding behavior is influenced by roadway type, driving conditions, and driver demographics. Scatter plot analyses showed that male drivers were more prevalent in the Situational and Habitual categories on lower-speed roads (30–35 mph) in Seattle, whereas females dominated the Incidental group in Texas. On higher-speed roads (55–60 mph), most drivers exhibited Regular or Casual patterns, with fewer instances of Situational speeding. Exploratory analyses linked specific personality and attitudinal factors to these patterns. Younger drivers and those scoring higher on reckless driving or road rage metrics were more likely to speed. Conversely, drivers who reported resisting the temptation to speed or feeling influenced by others to maintain speed limits sped less. Speeding was also more frequent during morning rush hours and on weekends. The study concludes that a behavior-based framework effectively organizes driver motivations, attitudes, and beliefs regarding speed. Drivers appear to monitor identifiable "set points," including posted limits, perceived ticket thresholds, and safe speeds, which vary by road type. These perceptions, combined with situational and personality factors, drive speeding decisions. This classification system provides a structured approach for understanding the complex relationship between driver characteristics and speeding behavior, offering insights for future safety interventions.

Key finding

Combining the share of trips with any speeding and the average per-trip speeding produced four driver types: incidental, situational, regular/casual, and habitual speeders.

Methodology

naturalistic

Sample size: 164

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 (9 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
• sensation seeking
• cultural cross national
• risk taking

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
• Methodological Resource: dataset resource
• Theoretical Contribution: theory or model