Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1539-6924.2012.01879.x
Title: Tree-based logistic regression approach for work zone casualty risk assessment
Authors: Weng, J.
Meng, Q. 
Wang, D.Z.W.
Keywords: Classification
Crash
Decision tree
Logistic regression
Risk
Work zone
Issue Date: Mar-2013
Source: Weng, J., Meng, Q., Wang, D.Z.W. (2013-03). Tree-based logistic regression approach for work zone casualty risk assessment. Risk Analysis 33 (3) : 493-504. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1539-6924.2012.01879.x
Abstract: This 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. © 2012 Society for Risk Analysis.
Source Title: Risk Analysis
URI: http://scholarbank.nus.edu.sg/handle/10635/59240
ISSN: 02724332
DOI: 10.1111/j.1539-6924.2012.01879.x
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