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The traffic forecasting model, when considered as a system with inputs of historical and current data and outputs of future data, behaves in a nonlinear fashion and varies with time of day. Traffic data are found to change abruptly during the transition times of entering or leaving rush hours. Accurate and real-time models are needed to approximate the(More)
The tensor completion problem is to recover a low-n-rank tensor from a subset of its entries. The main solution strategy has been based on the extensions of trace norm for the minimization of tensor rank via convex optimization. This strategy bears the computational cost required by the singular value decomposition (SVD) which becomes increasingly expensive(More)
Short-term traffic prediction plays a critical role in many important applications of intelligent transportation systems such as traffic congestion control and smart routing, and numerous methods have been proposed to address this issue in the literature. However, most, if not all, of them suffer from the inability to fully use the rich information in(More)
Feature selection has attracted significant attention in data mining and machine learning in the past decades. Many existing feature selection methods eliminate redundancy by measuring pairwise inter-correlation of features, whereas the complementariness of features and higher inter-correlation among more than two features are ignored. In this study, a(More)
Using cell phones as traffic probes is a promising Intelligent Transportation System technology. Compared with traditional traffic data collecting approaches, cellular probe has the advantage of the ready-to-use infrastructure and the wide coverage. This paper presents two Bayesian framework based traffic estimation models by the measurement of cell handoff(More)