Advanced accident prediction models and impacts assessment

Autor: Mario Catalano, Nabeel Shaikh, Ecaterina McCormick, Fabio Galatioto, Ryan Johnston
Rok vydání: 2018
Předmět:
Zdroj: IET Intelligent Transport Systems. 12:1131-1141
ISSN: 1751-9578
DOI: 10.1049/iet-its.2018.5218
Popis: 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.
Databáze: OpenAIRE
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