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Extraction of Hazardous Freeway Sections Using GPS-Based Probe Vehicle Speed Data  

Park, Jae-Hong (한양대학교 교통공학과)
Oh, Cheol (한양대학교 교통공학과)
Kim, Tae-Hyung (한국교통연구원 첨단교통연구실)
Joo, Shin-Hye (한양대학교 교통공학과)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.9, no.3, 2010 , pp. 73-84 More about this Journal
Abstract
This study presents a novel method to identify hazardous segments of freeway using global positioning system(GPS) based probe vehicle data. A variety of candidate contributing factors leading to higher potential of accident occurrence were extracted from the probe vehicle dataset. The research problem was defined as a classification problem, then a well-known classifier, bayesian neural network was adopted to solve the problem. A binary logistic regression technique was also used for selecting salient input variables. Test results showed that the proposed method is promising in extracting hazardous freeway sections. The outcome of this study will be effectively used for evaluating the safety of freeway sections and deriving countermeasures to prevent accidents.
Keywords
Traffic safety; speed; BLR(Binary Logistic Regression); probabilistic neural network; probe vehicle; CCR(Correct Classification Rate);
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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