A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash
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RESEARCH
Open Access
A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash and offense history data Reneta Slikboer* , Samuel D. Muir , S. S. M. Silva
and Denny Meyer
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 Keywords: Systematic review, Quality assessment tool, Crash, Traffic, Offense, Statistics, Statistical modeling, Driver offenses, Crash history
* Correspondence: [email protected] Centre for Mental Health, Faculty of Health Arts and Design, Swinburne University of Technology, PO Box 218, Mail H31, John St, Hawthorn, Victoria 3122, Australia © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence a
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