A New Traffic Prediction Method based on Dynamic Tensor Completion

Tan, Huachun; Wu, Yao; Feng, Guangdong; Wang, Wuhong

doi:10.1016/j.sbspro.2013.08.272

Cited by 29 publications

(11 citation statements)

References 19 publications

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“…A Tucker-based Coupled Matrix Tensor Factorization algorithm is used for jointly decomposition, recovery and estimation. Beyond the batch se ing, Tan et al [190] propose a Windows-Based Tensor Completion algorithm to tackle the short-term tra c prediction problem. ey form the problem as a third-order tensor (week×day×point) to leverage the strong similarity between the same day in di erent weeks.…”

Section: Social Computingmentioning

confidence: 99%

Tensor Completion Algorithms in Big Data Analytics

Song

Caverlee

et al. 2019

ACM Trans. Knowl. Discov. Data

174

118

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Tensor completion is a problem of lling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications have received wide a ention and achievement in areas like data mining, computer vision, signal processing, and neuroscience. In this survey, we provide a modern overview of recent advances in tensor completion algorithms from the perspective of big data analytics characterized by diverse variety, large volume, and high velocity. We characterize these advances from four perspectives: general tensor completion algorithms, tensor completion with auxiliary information (variety), scalable tensor completion algorithms (volume), and dynamic tensor completion algorithms (velocity). Further, we identify several tensor completion applications on real-world data-driven problems and present some common experimental frameworks popularized in the literature. Our goal is to summarize these popular methods and introduce them to researchers and practitioners for promoting future research and applications. We conclude with a discussion of key challenges and promising research directions in this community for future exploration.

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Section: Social Computingmentioning

confidence: 99%

Tensor Completion Algorithms in Big Data Analytics

Song

Caverlee

et al. 2019

ACM Trans. Knowl. Discov. Data

174

118

View full text Add to dashboard Cite

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“…Tensor models are generalizations of both vector models and matrix models as multidimensional models. In recent years, the use of tensors to represent multidimensional data with multimode features [1] has been shown to overcome the deficiencies of vector and matrix data forms with which it is difficult to characterize multidimensional features [2]. However, the multimode information of traffic flow can be analyzed in a tensor framework.…”

Section: Introductionmentioning

confidence: 99%

A Multimode Dynamic Short‐Term Traffic Flow Grey Prediction Model of High‐Dimension Tensors

Duan

Xiao

2019

Complexity

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Short-term traffic flow prediction is an important theoretical basis for intelligent transportation systems, and traffic flow data contain abundant multimode features and exhibit characteristic spatiotemporal correlations and dynamics. To predict the traffic flow state, it is necessary to design a model that can adapt to changing traffic flow characteristics. Thus, a dynamic tensor rolling nonhomogeneous discrete grey model (DTRNDGM) is proposed. This model achieves rolling prediction by introducing a cycle truncation accumulated generating operation; furthermore, the proposed model is unbiased, and it can perfectly fit nonhomogeneous exponential sequences. In addition, based on the multimode characteristics of traffic flow data tensors and the relationship between the cycle truncation accumulated generating operation and matrix perturbation to determine the cycle of dynamic prediction, the proposed model compensates for the periodic verification of the RSDGM and SGM grey prediction models. Finally, traffic flow data from the main route of Shaoshan Road, Changsha, Hunan, China, are used as an example. The experimental results show that the simulation and prediction results of DTRNDGM are good.

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“…Tensor decompositions are one of models that can naturally capture and model the spatiotemporal variance of traffic data. They are recently applied for solving many problems in relevant areas such as traffic flow prediction [2], data compression of urban traffic data [1], clustering and prediction of temporal evolution of global urban network [6], traffic speed data imputation [10], estimation of missing traffic volume [11,14,15,16,17,18], and traffic volume data outlier recovery [19]. However, to the best of our knowledge, Non-negative Tensor Factorization (NNTF) has never been applied to the mobility pattern extraction problem.…”

Section: Introductionmentioning

confidence: 99%

Mobility Mining Using Nonnegative Tensor Factorization

Nosratabadi

Fanaee-T

Gama

2017

Progress in Artificial Intelligence

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Mobility mining has lots of applications in urban planning and transportation systems. In particular, extracting mobility patterns enables service providers to have a global insight about the mobility behaviors which consequently leads to providing better services to the citizens. In the recent years several data mining techniques have been presented to tackle this problem. These methods usually are either spatial extension of temporal methods or temporal extension of spatial methods. However, still a framework that can keep the natural structure of mobility data has not been considered. Non-negative tensor factorizations (NNTF) have shown great applications in topic modelling and pattern recognition. However, unfortunately their usefulness in mobility mining is less explored. In this paper we propose a new mobility pattern mining framework based on a recent non-negative tensor model called BetaNTF. We also present a new approach based on interpretability concept for determination of number of components in the tensor rank selection process. We later demonstrate some meaningful mobility patterns extracted with the proposed method from bike sharing network mobility data in Boston, USA.

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A New Traffic Prediction Method based on Dynamic Tensor Completion

Cited by 29 publications

References 19 publications

Tensor Completion Algorithms in Big Data Analytics

Tensor Completion Algorithms in Big Data Analytics

A Multimode Dynamic Short‐Term Traffic Flow Grey Prediction Model of High‐Dimension Tensors

Mobility Mining Using Nonnegative Tensor Factorization

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