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Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network(More)
Ensemble learning of neural network is a learning paradigm where ensembles of several neural networks show improved generalization capabilities that outperform those of single networks. For deep learning of multi-layer neural networks, ensemble learning is still applicable. In addition, characteristics of deep neural networks can provide potential(More)
Nearest neighbor based nonparametric regression is a classic data-driven method for traffic flow prediction in intelligent transportation systems (ITS). Performances of those models depend heavily on the similarity or distance metric used to search nearest neighborhood. Metric learning algorithms have been developed to learn the distance metrics from data(More)
Traffic flow prediction is a fundamental component in Intelligent Transportation Systems (ITS). Nearest neighbor based nonparametric regression method is a classic data-driven method for traffic flow prediction. Modern data collection technologies provide the opportunity to represent various features of the nonlinear complex system which also bring(More)
Research on traffic data analysis is becoming more available and important. One of the key challenges is how to accurately decompose the high-dimensional, noisy observation traffic flow matrix into sub-matrices that correspond to different classes of traffic flow which builds a foundation for traffic flow prediction, abnormal data detection and missing data(More)
Deep learning has attracted a lot of attention in research and industry in recent years. Behind the success of deep learning, there is much space for improvement. It is difficult to identify if a testing sample can be represented by the deep network effectively before we examining the final result. In this paper, we proposed a dynamic boosting strategy(More)
Process neural network is widely used in modeling temporal process inputs in neural networks. Traditional process neural network is usually limited in structure of single hidden layer due to the unfavorable training strategies of neural network with multiple hidden layers and complex temporal weights in process neural network. Deep learning has emerged as(More)