Browsing by Author "Chen, Wen-Pin"

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A two-stage mining framework to explore key risk conditions on one-vehicle crash severity
(2013-01-01)
Chiou, Yu-Chiun
;
Lan, Lawrence W.
;
Chen, Wen-Pin
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
This paper proposes a two-stage mining framework to explore the key risk conditions that may have contributed to the one-vehicle crash severity in Taiwan's freeways. In the first stage, a genetic mining rule (GMR) model is developed, using a novel stepwise rule-mining algorithm, to identify the potential risk conditions that best elucidate the one-vehicle crash severity. In the second stage, a mixed logit model is estimated, using the antecedent part of the mined-rules as explanatory variables, to test the significance of the risk conditions. A total of 5563 one-vehicle crash cases (226 fatalities, 1593 injuries and 3744 property losses) occurred in Taiwan's freeways over 2003-2007 are analyzed. The GMR model has mined 29 rules for use. By incorporating these 29 mined-rules into a mixed logit model, we further identify one key safe condition and four key risk conditions leading to serious crashes (i.e., fatalities and injuries). Each key risk condition is discussed and compared with its adjacent rules. Based on the findings, some countermeasures to rectify the freeway's serious one-vehicle crashes are proposed. (C) 2012 Elsevier Ltd. All rights reserved.
IDENTIFYING RISK CONDITIONS TO CRASH SEVERITY: GENETIC MINING RULE APPROACH
(2010)
Chou, Yu-Chiun
;
Lan, Lawrence W.
;
Chen, Wen-Pin
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
This paper proposes a two-stage analytical framework to identify the critical risk conditions contributing to crash severity. The first stage develops a genetic mining rule (GMR) model to identify possible risk conditions best elucidating the degree of severity. The second stage then uses the mined risk conditions as dummy explanatory variables to formulate an ordered Probit model. The proposed two-stage analytical framework is applied to analyze the Taiwan's empirical one-vehicle crash data. A total of 38 rules are mined, which can achieve overall prediction rates of 75.10% in training and 73.80% in validation. Based on the results, six most critical risk conditions are identified and the corresponding countermeasures are addressed.