We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then we proposed a new real-time crash risk prediction model using decision tree method and adaptive neural network fuzzy inference system (ANFIS). By comparing several real-time crash risk prediction methods, it was found that our proposed method had higher precision than others. And the training error and testing error were minimum (0.280 and 0.291, resp.) when the data during 0 to 30 minutes prior to crash occurrence was collected and the decision tree-ANFIS method was applied to train and establish the real-time crash risk prediction model. The prediction accuracy of the crash occurrence could reach 65% when 0.60 was considered as the crash prediction threshold.
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Abstract Purpose Overcoming the flaws of current data management conditions in head and neck oncology could enable integrated informatio...
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Vol.83 No.3 from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/1TkQfWM via IFTTT
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Small size of metastatic lymph nodes with extracapsular spread greatly impacts treatment outcomes in oral squamous cell carcinoma patie...
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Guidelines for inpatient admission after pediatric tonsillectomy have been proposed to improve the safety of this procedure. This study exam...
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Madhavi Bhargava Journal of Family and Community Medicine 2017 24(2):131-131 from #AlexandrosSfakianakis via Alexandros G.Sfakianakis ...
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Publication date: Available online 14 May 2017 Source: Journal of Oral Biosciences Author(s): Hiromi Kimura-Suda, Teppi Ito BackgroundBo...
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