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http://dx.doi.org/10.14400/JDC.2022.20.5.039

Classifying the severity of pedestrian accidents using ensemble machine learning algorithms: A case study of Daejeon City  

Kang, Heungsik (Department of Mechatronics Engineering, Chungnam National University)
Noh, Myounggyu (Department of Mechatronics Engineering, Chungnam National University)
Publication Information
Journal of Digital Convergence / v.20, no.5, 2022 , pp. 39-46 More about this Journal
Abstract
As the link between traffic accidents and social and economic losses has been confirmed, there is a growing interest in developing safety policies based on crash data and a need for countermeasures to reduce severe crash outcomes such as severe injuries and fatalities. In this study, we select Daejeon city where the relative proportion of fatal crashes is high, as a case study region and focus on the severity of pedestrian crashes. After a series of data manipulation process, we run machine learning algorithms for the optimal model selection and variable identification. Of nine algorithms applied, AdaBoost and Random Forest (ensemble based ones) outperform others in terms of performance metrics. Based on the results, we identify major influential factors (i.e., the age of pedestrian as 70s or 20s, pedestrian crossing) on pedestrian crashes in Daejeon, and suggest them as measures for reducing severe outcomes.
Keywords
Pedestrian crashes; Crash severity classification; Ensemble machine learning; Daejeon city; Random Forests; AdaBoost;
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Times Cited By KSCI : 4  (Citation Analysis)
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