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

Abstract

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

A Crash Data Analysis through a Comparative Application of Regression and Neural Network Models

“…In the review paper by Slikboer et al (2020) [36], twenty studies on the prediction of road crashes were taken into consideration, and among them only one reported an independent variable (driver gender) which was also used in our research. In the paper by Zhang et al (2018) [37], road and meteorological conditions were the only significant fields to be compared.…”

Proceedings of the 2023 Australasian Road Safety Conference - Cairns, Queensland, Australia

“…Among the 246 eligible papers, only 36 met the requirements of this review. The quality of these papers was assessed based on the Statistical Analyses and Methods in the Published Literature (SAMPL) guidelines (Altman & Sculz, 2014) as modified by Slikboer et al (2020).…”

Crash severity analysis of vulnerable road users using machine learning

“…It is beyond the scope of this study to present a detailed literature review of these statistical modelling. Please see Slikboer, Muir [ 34 ] for a detailed literature review on these studies. More recently, several studies have also adopted machine learning-based techniques to identify the important crash features for crash severity [ 35 – 38 ].…”