Literature DB >> 11579975

A nationwide survey of self-reported red light running: measuring prevalence, predictors, and perceived consequences.

B E Porter1, T D Berry.   

Abstract

A United States probability sample of 880 licensed drivers participated in a telephone survey of red light running perceptions and behaviors. Despite most drivers believing red light running was problematic and dangerous, approximately one in five respondents reported running one or more red lights when entering the last ten signalized intersections. Among several demographic and attitude variables, only age group predicted recent red light running. Specifically, younger respondents were more likely to be violators. Drivers also reported being more likely to run red lights when alone, and were typically in a hurry when speeding up to be beat red lights. Contrary to expectations, frustration was not as important for predicting red light running as it was for other driving behaviors, such as speeding, tailgating, weaving, and gesturing angrily at others. Additionally, drivers perceived and received few consequences for running red lights. Less than 6% had received a traffic ticket for red light running and most believed that police would catch less than 20% of violators. Slightly more than one in ten had been involved in a red light running crash. Respondents most commonly suggested legal initiatives to reduce red light running. Accordingly, we recommend traffic safety experts pursue interventions that apply immediate and consistent negative consequences to violators to change the public's red light running perceptions and behavior.

Mesh:

Year:  2001        PMID: 11579975     DOI: 10.1016/s0001-4575(00)00087-7

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  6 in total

1.  Is speeding a form of gambling in adolescents?

Authors:  David S Husted; Mark S Gold; Kimberly Frost-Pineda; Mary A Ferguson; Mark C K Yang; Nathan A Shapira
Journal:  J Gambl Stud       Date:  2006-06-29

2.  Child passengers killed in reckless and alcohol-related motor vehicle crashes.

Authors:  Tara Kelley-Baker; Eduardo Romano
Journal:  J Safety Res       Date:  2014-01-04

3.  Predicting Driver Behavior during the Yellow Interval Using Video Surveillance.

Authors:  Juan Li; Xudong Jia; Chunfu Shao
Journal:  Int J Environ Res Public Health       Date:  2016-12-06       Impact factor: 3.390

4.  Red-Light-Running Crashes' Classification, Comparison, and Risk Analysis Based on General Estimates System (GES) Crash Database.

Authors:  Yuting Zhang; Xuedong Yan; Xiaomeng Li; Jiawei Wu; Vinayak V Dixit
Journal:  Int J Environ Res Public Health       Date:  2018-06-19       Impact factor: 3.390

5.  Investigating influence factors of traffic violations at signalized intersections using data gathered from traffic enforcement camera.

Authors:  Chuanyun Fu; Hua Liu
Journal:  PLoS One       Date:  2020-03-04       Impact factor: 3.240

6.  Older drivers and failure to stop at red lights.

Authors:  Sheila K West; Daniel V Hahn; Kevin C Baldwin; Donald D Duncan; Beatriz E Munoz; Kathleen A Turano; Shirin E Hassan; Cynthia A Munro; Karen Bandeen-Roche
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2009-10-12       Impact factor: 6.053

  6 in total

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