The Journey from Traffic Offender to Severe Road Trauma Victim: Destiny or Preventive Opportunity?

Ho, Kwok M.; Rao, Sudhakar; Burrell, Maxine; Weeramanthri, Tarun

doi:10.1371/journal.pone.0122652

Cited by 11 publications

(11 citation statements)

References 17 publications

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“…They also found out that the costliest traffic violations were speeding-related violations. Associations between traffic offenses and involvement in severe crashes were found in the previous studies [21]. Murray identified violations that were associated with an increase in truck crash likelihood [22].…”

Section: Literature Reviewmentioning

confidence: 92%

Predicting Truck At-Fault Crashes Using Crash and Traffic Offence Data

Rezapour¹,

Wulff²,

Ksaibati³

2018

TOTJ

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Introduction:The number of truck-related injuries and deaths can be reduced by understanding the factors that contribute to the higher risk of truck-related crashes and violations. Truck drivers are at fault of more than 80% of all the truck crashes on Wyoming interstates, and the literature review indicated that in order to identify appropriate countermeasure to crashes, each crash type should be analyzed individually. The literature review also revealed that relationships exist between driving records and driver culpability in crashes.Method:This study employed two approaches to identify contributory factors to truck-at-fault fatal and injury crashes, and truck-related violations. Interstate 80, a Wyoming corridor in a mountainous area with one of the highest truck crash rates in the US, was selected as a case study. Only truck-at-fault crashes and specific types of truck-related violations were considered in this study. The analyses include two approaches. First, the logistic regression model was employed to explore vehicle, driver, crash, and environmental characteristics that contribute to truck-at-fault fatal and injury crashes. Second, truck violations were used as a proxy for truck crashes to examine the tendency to violate truck-related traffic laws in relation to driver and temporal characteristics. Based on the literature, only violations associated with higher risk of severe crashes were included in the analyses. The included violations accounted for more than 70% of all the violations.Result:This study considered more than 30 variables and found that only 10 variables impact truck-at-fault crashes. These factors included: gender, history of past violation, crashes involving multiple vehicles, exceeding the speed limit, occupant distraction, driver ejection, fatigued driving, non-seat belt usage, overturn, and head-on collision. Results of the second analysis indicated that both residency and time of crash are factors that impact truck-related violations. Results of the analysis also indicated that both residency and time of the crash are factors that impact truck-related violations.

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Section: Literature Reviewmentioning

confidence: 92%

Predicting Truck At-Fault Crashes Using Crash and Traffic Offence Data

Rezapour¹,

Wulff²,

Ksaibati³

2018

TOTJ

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show abstract

“…Studies on the effectiveness of penalty systems to increase road safety show that they can be successful in reducing dangerous driving behaviours, such as drink driving9 10 and associated road transport crashes 11–14. However, individual-level studies show that drivers who incur offences are also at increased risk of car crash 15–23. It has therefore been suggested that driving offences are an indicator of risky driving behaviours15 16 that, in turn, are associated with an increased risk of crash 24–26…”

Section: Introductionmentioning

confidence: 99%

“…However, individual-level studies show that drivers who incur offences are also at increased risk of car crash 15–23. It has therefore been suggested that driving offences are an indicator of risky driving behaviours15 16 that, in turn, are associated with an increased risk of crash 24–26…”

Section: Introductionmentioning

confidence: 99%

Driving offences and risk of subsequent crash in novice drivers: the DRIVE cohort study 12-year follow-up

et al. 2022

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BackgroundPenalties are a key component to improve road user safety, but previous studies suggested that they might not be successful in reducing crashing in offending drivers. However, these studies were not able to consider important crash risk factors in the analysis that might confound the results. Using data from a large prospective cohort study of young drivers in New South Wales, Australia, we explored if novice drivers with driving offences have a higher rates of car crash and if these differences are explained by established crash risk factors.MethodsWe used data from a 2003/2004 Australian survey of young drivers, linked to police reported offence and crash data, hospital data and deaths data up to 2016. We used Poisson regression models adjusted for confounders to estimate the association between driving offences during 2003–2006 with car crash during 2007–2016.ResultsThe study cohort comprised 20 781 young drivers of whom 7860 drivers (37.8%) had at least one driving offence and 2487 (12.0%) were involved in at least one crash. After adjusting for confounders in the regression model, drivers with three or more driving offences had 2.25 (95% CI 1.98 to 2.57), 2.87 (95% CI 1.60 to 5.17) and 3.28 (95% CI 2.28 to 4.72) times higher rates of any crash, crashes that resulted in hospital admission or death and single vehicle crashes compared with drivers with no driving offences.ConclusionMeasures that successfully mitigate the underlying risk factors for both, crashes and offences, have the potential to improve road safety.

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“…Analysis of state-wide crash and traffic offence data from California, found earlier traffic convictions to be slightly more predictive of subsequent crashes than previous crashes [ 4 , 9 – 11 ]. A recent cohort study examining driver licence history of 10,063 victims of road trauma, found the number of prior traffic offences to be significantly associated with subsequent involvement in a severe crash, particularly alcohol-related road trauma [ 12 ]. Moreover, additional offences incurred following an initial crash was also associated with a greater likelihood of involvement in a subsequent crash.…”

Section: Introductionmentioning

confidence: 99%

Predicting Crashes Using Traffic Offences. A Meta-Analysis that Examines Potential Bias between Self-Report and Archival Data

Barraclough

Wåhlberg²,

Freeman

et al. 2016

PLoS ONE

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BackgroundTraffic offences have been considered an important predictor of crash involvement, and have often been used as a proxy safety variable for crashes. However the association between crashes and offences has never been meta-analysed and the population effect size never established. Research is yet to determine the extent to which this relationship may be spuriously inflated through systematic measurement error, with obvious implications for researchers endeavouring to accurately identify salient factors predictive of crashes.Methodology and Principal FindingsStudies yielding a correlation between crashes and traffic offences were collated and a meta-analysis of 144 effects drawn from 99 road safety studies conducted. Potential impact of factors such as age, time period, crash and offence rates, crash severity and data type, sourced from either self-report surveys or archival records, were considered and discussed. After weighting for sample size, an average correlation of r = .18 was observed over the mean time period of 3.2 years. Evidence emerged suggesting the strength of this correlation is decreasing over time. Stronger correlations between crashes and offences were generally found in studies involving younger drivers. Consistent with common method variance effects, a within country analysis found stronger effect sizes in self-reported data even controlling for crash mean.SignificanceThe effectiveness of traffic offences as a proxy for crashes may be limited. Inclusion of elements such as independently validated crash and offence histories or accurate measures of exposure to the road would facilitate a better understanding of the factors that influence crash involvement.

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The Journey from Traffic Offender to Severe Road Trauma Victim: Destiny or Preventive Opportunity?

Cited by 11 publications

References 17 publications

Predicting Truck At-Fault Crashes Using Crash and Traffic Offence Data

Predicting Truck At-Fault Crashes Using Crash and Traffic Offence Data

Driving offences and risk of subsequent crash in novice drivers: the DRIVE cohort study 12-year follow-up

Predicting Crashes Using Traffic Offences. A Meta-Analysis that Examines Potential Bias between Self-Report and Archival Data

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