Abstract
BackgroundExpenditure 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.MethodsOnly 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.ResultsOne 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.ConclusionsIt 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 registrationPROSPERO CRD42019137081
Highlights
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
Agreement between the two reviewers (RS and Samuel Muir (SM)) of the full texts for inclusion was low (k = 0.469, p < 0.00). These disagreements were resolved by a third author (SM), resulting in twenty studies being included in the quality assessment, comprising of data from a total of 2,379,862 individuals
The review identified that multicollinearity, model validation, and appropriate methods for the selection of predictor variables remain problematic in studies predicting fatal and serious injury (FSI) crashes from offense and crash history
Summary
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. Expenditure on driver-related behavioral interventions and road use policy is often justified by their impact on the frequency of fatal and serious injury (FSI) crashes. Traffic offenses (e.g., speeding and disobeying traffic lights) are much more frequent than FSI crashes [2, 3]. This has led to organizations using offending patterns as a proxy measure to evaluate the effectiveness of new interventions and policies targeting the reduction of FSI crashes
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