Development of Traffic Diagnosis Model to Support Traffic Control System applying Artificial Intelligence Model

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This research investigates to apply Bayesian Network technique to develop Traffic Diagnosis Model which detect traffic congestion caused by incidents on Tokyo Metropolitan Expressway. Especially this research focused to detect traffic congestion caused by traffic accident using traffic detector data with learning past data to estimate conditional probability distributions of occurrence the situation by traffic accident at Yoyogi entrance section on route No. 4 of Tokyo Metropolitan Expressway. The hit ratio of detection of traffic congestion by traffic accident was around 40% which is almost same level of accuracy compared with existing research, but the analysis shows the requirements to improve accuracy of proposed model compared with existing study. And it is also recommended to consider traffic condition of whole of expressway network.