Multidimensional CX Decomposition of Tensors

Couras, Maria F. K. B.; Pinho, Pablo H. de; Favier, Gérard; Costa, João Paulo; Zarzoso, Vicente; Almeida, André L. F. de

doi:10.1109/wcnps.2019.8896279

Cited by 1 publication

(2 citation statements)

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“…Another interesting avenue for future research is to explore the possibility of incorporating other traffic data, such as vehicle count and vehicular types, into the methodology for a more comprehensive representation of the traffic condition at specific detectors, thus resulting in a better representation of the entire network. This involves methods like column-based decomposition for multidimensional tensors, proposed by Couras et al [12] in 2019.…”

Section: Discussionmentioning

confidence: 99%

“…Another issue is that the author only tested a limited range of compression ratios from 2 to 10 [2,10], which is not sufficient to depict the performance of the proposed method. In addition to leverage-based column selection, Couras et al proposed [12] an algorithm to perform the approximation of the tensors based on the CX decomposition for matrices. Han and Huang [13] proposed a road network compression method to improve the efficiency of data processing based on correlation analysis and CX decomposition, which is then integrated into a deep learning network to predict the short-term traffic flow.…”

Section: Introductionmentioning

confidence: 99%

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Representing and Inferring Massive Network Traffic Condition: A Case Study in Nashville, Tennessee

Zhang,

Gu,

Han

2023

Algorithms

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Intelligent transportation systems (ITSs) usually require monitoring of massive road networks and gathering traffic data at a high spatial and temporal resolution. This leads to the accumulation of substantial data volumes, necessitating the development of more concise data representations. Approaches like principal component analysis (PCA), which operate within subspaces, can construct precise low-dimensional models. However, interpreting these models can be challenging, primarily because the principal components often encompass a multitude of links within the traffic network. To overcome this issue, this study presents a novel approach for representing and indexing network traffic conditions through weighted CUR matrix decomposition integrated with clustering analysis. The proposed approach selects a subset group of detectors from the original network to represent and index traffic condition through a matrix decomposition method, allowing for more efficient management and analysis. The proposed method is evaluated using traffic detector data from the city of Nashville, TN. The results demonstrate that the approach is effective in representing and indexing network traffic conditions, with high accuracy and efficiency. Overall, this study contributes to the field of network traffic monitoring by proposing a novel approach for representing massive traffic networks and exploring the effects of incorporating clustering into CUR decomposition. The proposed approach can help traffic analysts and practitioners to more efficiently manage and analyze traffic conditions, ultimately leading to more effective transportation systems.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%