Driver speeding typologies by roadway behaviours and beliefs: A latent class analysis with a multistate sample of U.S. adults

Colleen M. Peterson, Toben F. Nelson, Mark A. Pereira

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Speeding has consistently contributed to a high number of motor vehicle crashes and subsequent injuries and deaths in the U.S. Identifying types of drivers related to speeding behaviour may help target interventions to reduce speeding. Typology of U.S. driver speeders have examined very specifically speeding behaviours and speeding-related attitudes. This exploratory work used latent class analysis (LCA) to examine how other driving behaviours and attitudes cluster around speeding behaviours to determine speeder typologies, which may lend a more holistic perspective to speeder types. Predicted class assignments were evaluated for associations with demographic and personality factors. The LCA resulted in four driver typologies, which we labelled: Externally Motivated (40.7%), Non-Reactors (26.2%), Perceived Invulnerable (24.3%), and Perceived Vulnerable (8.9%). The Externally Motivated and Non-Reactors typologies had the highest probability of reporting extreme speeding. The Externally Motivated may be intervened upon with messaging about reducing risks crashes and injuring passengers, while the Perceived Vulnerable class already exhibit several risk-averse behaviours that self-limits their speeding behaviour. Class placement was associated with age, self-reported speeding frequency, receipt of speeding violations. The resulting U.S. driver typologies advances the literature by demonstrating that non-speeding driver behaviours and attitudes cluster with speeding behaviours, which altogether can inform more nuanced and effective anti-speeding campaigns.

Original languageEnglish (US)
Pages (from-to)373-383
Number of pages11
JournalTransportation Research Part F: Traffic Psychology and Behaviour
Volume81
DOIs
StatePublished - Aug 1 2021

Bibliographical note

Funding Information:
This work was supported by the University of Minnesota , Minneapolis, MN.

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • Cluster analysis
  • Driver behaviour
  • Driver psychology
  • Latent class
  • Risk factors

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