The effect of traffic tickets on road traffic crashes

Factor, Roni

doi:10.1016/j.aap.2013.11.010

Cited by 70 publications

(30 citation statements)

References 40 publications

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“…Cooper [34] found that fourteen conviction types increase the chance of being in a subsequent FSI crash; speeding and DWI convictions were the most important. These findings are supported by Factor [41], who used traffic infringement tickets as a predictor variable, and Rajalin [33], who compared drivers who had had an FSI crash to those who had not. Further nuance to the role of offense history is added by Kim et al [36].…”

Section: Qualitative Synthesis Of Study Outcomesmentioning

confidence: 77%

“…There are several reasons for the popularity of logistic regression. Logistic regression has many different forms allowing its use for conditional logistic regression with matched-pairs data [32,45]; ordinal and nominal logistic regression when the dependent variable has several categories [45,47]; direct logistical regression when no predictor variables are considered more or less important than the others [37]; sequential logistic regression when confounding variables need to be controlled [41,43]; stepwise logistic regression when an exploratory approach is needed [49]; and censored regression when data for the dependent variable is incomplete [36]. Further, the assumptions for logistic regression are lenient.…”

Section: Strengths Of Logistic Regression In This Contextmentioning

confidence: 99%

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A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash and offense history data

et al. 2020

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Background Expenditure on driver-related behavioral interventions and road use policy is often justified by their impact on the frequency of fatal and serious injury crashes. Given the rarity of fatal and serious injury crashes, offense history, and crash history of drivers are sometimes used as an alternative measure of the impact of interventions and changes to policy. The primary purpose of this systematic review was to assess the rigor of statistical modeling used to predict fatal and serious crashes from offense history and crash history using a purpose-made quality assessment tool. A secondary purpose was to explore study outcomes. Methods Only studies that used observational data and presented a statistical model of crash prediction from offense history or crash history were included. A quality assessment tool was developed for the systematic evaluation of statistical quality indicators across studies. The search was conducted in June 2019. Results One thousand one hundred and five unique records were identified, 252 full texts were screened for inclusion, resulting in 20 studies being included in the review. The results indicate substantial and important limitations in the modeling methods used. Most studies demonstrated poor statistical rigor ranging from low to middle quality. There was a lack of confidence in published findings due to poor variable selection, poor adherence to statistical assumptions relating to multicollinearity, and lack of validation using new data. Conclusions It was concluded that future research should consider machine learning to overcome correlations in the data, use rigorous vetting procedures to identify predictor variables, and validate statistical models using new data to improve utility and generalizability of models. Systematic review registration PROSPERO CRD42019137081

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Section: Qualitative Synthesis Of Study Outcomesmentioning

confidence: 77%

Section: Strengths Of Logistic Regression In This Contextmentioning

confidence: 99%

A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash and offense history data

et al. 2020

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“…e results of this study also show that up to 93% of drivers increase the risk of crashes, depending on the type of vehicles, when traffic violation rates increase if all other variables are kept constant. Similarly, many researchers have found a positive relationship between traffic violations and crash occurrence [6,[47][48][49][50]. However, most of the studies have been conducted on general drivers; only a few studies have been conducted on commercial vehicle drivers [10].…”

Section: Resultsmentioning

confidence: 99%

Factors Affecting Crash Involvement of Commercial Vehicle Drivers: Evaluation of Commercial Vehicle Drivers’ Characteristics in South Korea

Lee

Yeo

Yun

et al. 2020

Journal of Advanced Transportation

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The aim of this study was to evaluate the effects of driver-related factors on crash involvement of four different types of commercial vehicles—express buses, local buses, taxis, and trucks—and to compare outcomes across types. Previous studies on commercial vehicle crashes have generally been focused on a single type of commercial vehicle; however, the characteristics of drivers as factors affecting crashes vary widely across types of commercial vehicles as well as across study sites. This underscores the need for comparative analysis between different types of commercial vehicles that operate in similar environments. Toward these ends, we analyzed 627,594 commercial vehicle driver records in South Korea using a mixed logit model able to address unobserved heterogeneity in crash-related data. The estimated outcomes showed that driver-related factors have common effects on crash involvement: greater experience had a positive effect (diminished driver crash involvement), while traffic violations, job change, and previous crash involvement had negative effects. However, the magnitude of the effects and heterogeneity varied across different types of commercial vehicles. The findings support the contention that the safety management policy of commercial drivers needs to be set differently according to the vehicle type. Furthermore, the variables in this study can be used as promising predictors to quantify potential crash involvement of commercial vehicles. Using these variables, it is possible to proactively identify groups of accident-prone commercial vehicle drivers and to implement effective measures to reduce their involvement in crashes.

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“…Four self-report studies differentiated between major and minor crashes, recording data for both. A complete list of original data, country of origin, sample characteristics and other key variables from self-report studies is provided in Table 2 and for archival studies in Table 3 [ 4 , 8 , 10 , 11 , 15 , 17 , 20 , 40 , 58 – 60 , 63 – 152 ].…”

Section: Resultsmentioning

confidence: 99%

“…Drivers involved in fatal crashes have been found to have more prior traffic violation convictions than non-culpable drivers [ 5 , 6 ]. Drivers understood to have previously incurred a high number of traffic offences have been strongly associated with being responsible for a subsequent crash [ 7 , 8 ]. 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 ].…”

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 effect of traffic tickets on road traffic crashes

Cited by 70 publications

References 40 publications

A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash and offense history data

A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash and offense history data

Factors Affecting Crash Involvement of Commercial Vehicle Drivers: Evaluation of Commercial Vehicle Drivers’ Characteristics in South Korea

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

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