Injury severity prediction from two-vehicle crash mechanisms with machine learning and ensemble models

Ji Ang and David Levinson

No 2022-01, Working Papers from University of Minnesota: Nexus Research Group

Abstract: Machine learning algorithms aim to improve the power of predictors over conventional regression models. This study aims to tap the predictive potential of crash mechanism-related variables using ensemble machine learning models. The results demonstrate selected models can predict severity at a high level of accuracy. The stacking model with a linear blender is preferred for the designed ensemble combination. Most bagging, boosting, and stacking algorithms perform well, indicating ensemble models are capable of improving upon individual models.

Keywords: Injury severity; machine learning algorithms; vehicle crashes; ensemble technique; crash mechanisms (search for similar items in EconPapers)
JEL-codes: R41 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

Published in IEEE Open Journal of Intelligent Transportation Systems (Volume 1)

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http://dx.doi.org/10.1109/OJITS.2020.3033523 First version, 2020 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:nex:wpaper:injuryseverity

DOI: 10.1109/OJITS.2020.3033523

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