Road traffic accident prediction modelling: a literature review

Yannis, George; Dragomanovits, Anastasios; Laiou, Alexandra; Torre, Francesca La; Domenichini, Lorenzo; Richter, Thomas; Ruhl, Stephan; Graham, Daniel J.; Karathodorou, Niovi

doi:10.1680/jtran.16.00067

Cited by 34 publications

(15 citation statements)

References 25 publications

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“…Language and geographical bias limited this review. Similar to other reviews of related topics [ 20 , 53 ], the bulk of the research was conducted in the USA. We considered only articles written in English and the grey literature review was confined to higher-income countries with lower road death rates.…”

Section: Recommendations For Future Research Of Fsi Crashes Using Offmentioning

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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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: Recommendations For Future Research Of Fsi Crashes Using Offmentioning

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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show abstract

“…Mujalli and de Oña [7] summarized 19 modelling techniques applied to the accident severity analysis, and the performance of these models was compared according to seven principles. Yannis et al [8] performed an integrate literature review on road safety in terms of theoretical methods, model implementations, data sources and obtained results. Although various models may be utilized for studies of various purposes, most modelling involves discovering relationships between factors, and predicting outcomes.…”

Section: Safety Analysis On Roadwaysmentioning

confidence: 99%

Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks

Zhu

Zhang

et al. 2019

Sustainability

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Crashes that occur on curved roadways are often more severe than straight road accidents. Previously, most studies focused on the associations between curved sections and roadway geometric characteristics. In this study, significant factors such as driver behavior, roadway features, vehicle factors, and environmental characteristics are identified and involved in analyzing traffic accident severity. Bayesian network analysis was conducted to deal with data, to explore the associations between variables, and to make predictions using these relationships. The results indicated that factors including point of impact, site of location, accident side of road, alcohol/drugs condition, etc., are relatively critical in crashes on horizontal curves. Accident severity increases when crashes occur on bridges. The sensitivity of accident severity to vehicle use, traffic control, point of impact, and alcohol/drugs condition is relatively high. Moreover, a combination of negative factors will aggravate accident severities. The results also proposed some suggestions regarding the design of vehicles, as well as the construction and improvement of curved roadways.

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“…To sum up, road safety impact assessment should be based on state-of-the-art assessment approach, using accident prediction models (APMs) and accident modification factors (AMFs). These elements constitute the foundations of evidence-based road safety management [28,45,96]. However, most of existing APMs and AMFs were developed in North America, Australasia or North-Western Europe [95], and it is known that these results may not be easily transferable [40,59].…”

Section: Road Safety Impact Assessmentmentioning

confidence: 99%

Safety assessment of Czech motorways and national roads

Ambros

Turek

Brich

et al. 2019

Eur. Transp. Res. Rev.

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Purpose: Czech motorways and national roads form the primary road network, which is critical in terms of safety. To be able to rationally manage network safety in both planning and operation stages, quality network-wide data and tools are needed. While such tools already exist in some countries, their transferability is limited. Authors therefore collected data and used it to develop tools, which allowed conducting state-of-the-art road safety impact assessment and network safety ranking in the Czech conditions. In addition to primary road network, focus was widened to include also secondary roads, in order to enable assessment of impacts on adjacent road network. Methods: Accident, road and traffic data was collected, using not only existing databases, but also including own collection of traffic volumes on motorway interchanges. Data was used to develop the tools, based on accident prediction models and accident modification factors. Results: The final accident prediction models and accident modification factors enabled conducting road safety impact assessment, for which simple on-line tool was also developed. For network safety ranking, accident prediction models were applied according to the Empirical Bayes method, in order to determine potential for safety improvement of the studied road network elements, with the final priority list visualized in an on-line map. Both outputs are shortly presented in the paper. Conclusions: Data and sample size limitations lead to some compromises in modelling, such as using fixed proportions of observed accident severities or omitted variables. Nevertheless, the study established the practical framework for both road safety impact assessment and network safety ranking. It may serve as an example for other member countries, which also lack their local tools. Follow-up studies may focus on future model updating and improvements, as well as development of local accident modification factors.

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Road traffic accident prediction modelling: a literature review

Cited by 34 publications

References 25 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

Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks

Safety assessment of Czech motorways and national roads

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