dc.contributor.authorWeng, Jinxian.
dc.contributor.authorMeng, Qiang.
dc.contributor.authorWang, David Zhi Wei
dc.date.accessioned2013-11-15T06:18:37Z
dc.date.available2013-11-15T06:18:37Z
dc.date.copyright2013en_US
dc.date.issued2013
dc.identifier.citationWeng, J., Meng, Q., & Wang, D. Z. W. (2013). Tree-based logistic regression approach for work zone casualty risk assessment. Risk analysis, 33(3), 493-504.en_US
dc.identifier.issn0272-4332en_US
dc.identifier.urihttp://hdl.handle.net/10220/17683
dc.description.abstractThis study presents a tree-based logistic regression approach to assessing work zone casualty risk, which is defined as the probability of a vehicle occupant being killed or injured in a work zone crash. First, a decision tree approach is employed to determine the tree structure and interacting factors. Based on the Michigan M-94\I-94\I-94BL\I-94BR highway work zone crash data, an optimal tree comprising four leaf nodes is first determined and the interacting factors are found to be airbag, occupant identity (i.e., driver, passenger), and gender. The data are then split into four groups according to the tree structure. Finally, the logistic regression analysis is separately conducted for each group. The results show that the proposed approach outperforms the pure decision tree model because the former has the capability of examining the marginal effects of risk factors. Compared with the pure logistic regression method, the proposed approach avoids the variable interaction effects so that it significantly improves the prediction accuracy.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesRisk analysisen_US
dc.subjectDRNTU::Engineering::Civil engineering
dc.titleTree-based logistic regression approach for work zone casualty risk assessmenten_US
dc.typeJournal Article
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1111/j.1539-6924.2012.01879.x


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