학위논문 (박사)-- 서울대학교 대학원 공과대학 건설환경공학부, 2017. 8. 고승영.Traffic state prediction is an important issue in traffic operations. One of the main purposes of traffic operations is to prevent flow breakdown. Therefore, it is necessary to perform traffic state predictions that reflects the stochastic process of traffic flow. However, traffic state transition is affected complexly and simultaneously by many factors, which lead to a lack of understanding and accurate prediction. Meanwhile, the Bayesian network is a methodology that not only is suitable for a problem with uncertainty but also can improve the understanding of a problem. Also, it is possible to derive fair probability with incomplete information, which allows the analysis of various situations. In this study, we developed a traffic state prediction model using the Bayesian network to reflect dynamic and stochastic traffic flow characteristics. In order to improve the structure of the Bayesian network, which has been used simply in transportation problems, we proposed a modeling procedure using mixture of Gaussians (MOGs). Also, spatially extended variables were used to consider the spatiotemporal evolution of traffic flow pattern. In particular, traffic state identification was performed by estimating the link speed in order to consider the spatial propagation of congestion. In the performance evaluation, the Bayesian network has better performance than logistic regression and has the same level of performance as artificial neural network based on machine learning. Also, by performing sensitivity analyses, we provided the understanding of traffic state prediction and the guidelines for model improvement. Therefore, the Bayesian network developed in this study can be considered as a traffic state prediction model with good prediction accuracy and provides insights for traffic state prediction.Chapter 1. Introduction 1
1.1 Research background and purpose 1
1.2 Research scope and procedure 4
Chapter 2. Literature Review 8
2.1 Characteristics of traffic state 8
2.2 Traffic state estimation and prediction 14
2.3 Bayesian network 37
2.4 Originality of this research 41
Chapter 3. Data Collection and Preparation 46
3.1 Data collection and validity check 46
3.2 Traffic state identification 47
3.3 Data Description 63
Chapter 4. Bayesian Network Modeling 66
4.1 Modeling procedure 66
4.2 Description of interface mechanism 69
4.3 Module design 74
4.4 Eliciting the structure 81
4.5 Verification 81
4.6 Parameter learning 85
Chapter 5. Model Evaluation 87
5.1 Evaluation results 87
5.2 Comparison with other methodologies 92
5.3 Sensitivity analysis 104
Chapter 6. Conclusions 127
6.1 Summary 127
6.2 Guidelines for traffic state prediction 128
6.3 Limitations of the study 129
6.4 Applications and future research 130
References 135Docto
