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Citation |
Halbersberg D, Lerner B. Accid. Anal. Prev. 2019; 129: 350-361. |
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Affiliation |
Department of Industrial Engineering & Management, Ben-Gurion University of the Negev, Israel. Electronic address: boaz@bgu.ac.il. |
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Copyright |
(Copyright © 2019, Elsevier Publishing) |
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DOI |
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PMID |
31201968 |
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Abstract |
While young drivers (YDs) constitute ∼10% of the driver population, their fatality rate in motorcycle accidents is up to three times higher. Thus, we are interested in predicting fatal motorcycle accidents (FMAs), and in identifying their key factors and possible causes. Accurate prediction of YD FMAs from data by risk minimization using the 0/1 loss function (i.e., the ordinary classification accuracy) cannot be guaranteed because these accidents are only ∼1% of all YD motorcycle accidents, and classifiers tend to focus on the majority class of minor accidents at the expense of the minority class of fatal ones. Also, classifiers are usually uninformative (providing no information about the distribution of misclassifications), insensitive to error severity (making no distinction between misclassification of fatal accidents as severe or minor), and limited in identifying key factors. We propose to use an information measure (IM) that jointly maximizes accuracy and information and is sensitive to the error distribution and severity. Using a database of ∼3600 motorcycle accidents, a Bayesian network classifier optimized by IM predicted FMAs better than classifiers maximizing accuracy or other predictive or information measures, and identified fatal accident key factors and causal relations. Language: en |
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Keywords |
Bayesian network; Fatal accidents; Information measure; Key factors; Machine learning; Motorcycle; Prediction; Young drivers |