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access icon free Advanced accident prediction models and impacts assessment

© The Institution of Engineering and Technology
Received 23/01/2018, Accepted 18/07/2018, Revised 09/05/2018, Published 06/09/2018

This study presents an innovative set of models for accident prediction which are at the core of a web-based platform for road safety simulation and predictions. Specifically, insights into road hazard prediction are given comparing the latest developments of machine learning research to econometric modelling. The paper provides an overview of the above web-platform as well as the description of its built-in models and the early findings of comparing machine learning and econometric methods with respect to crash severity prediction. The original specification of the proposed predictive models embeds, on top of traditional predictors, complex inputs, sporadically or never encountered in previous studies, related to demographics, land use, roadway geometry, traffic control and accident circumstances (special conditions on the road, etc.). The final outcome reveals high accuracy at national level in forecasting the number of casualties from a road crash and its severity. The related models have proved less effective, instead, in those contexts where road collision phenomena turn out exceptional, thus moving away from the national mean behaviour. Finally, the comparison between statistical parametric and machine learning methods, at this early stage is limited to crash severity classification and has pointed out a clear superiority of the parametric approach.

Inspec keywords: road traffic; road accidents; road safety; learning (artificial intelligence); regression analysis; traffic engineering computing; econometrics

Other keywords: accidents prediction; machine learning; road hazard prediction; statistical parametric method; road accident phenomena; econometric methods; statistical parametric models; severity modelling; road collision phenomena; road safety policy simulation; web-based platform; road crash; impacts assessment; accident circumstances; econometric models; severity prediction

Subjects: Other topics in statistics; Traffic engineering computing; Knowledge engineering techniques

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