An orthogonally equivariant estimator of the covariance matrix in high dimensions and for small sample sizes

Banerjee, Samprit; Monni, Stefano

doi:10.1016/j.jspi.2020.10.006

Cited by 1 publication

(2 citation statements)

References 28 publications

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“…(3) The final step entails determining the traffic state utilizing both the clustering model and the dimensionreduced data from the preceding step. To ensure stable cluster results, the bootstrap method (Banerjee and Monni, 2021) is employed to identify an optimal cluster center. Further elaboration of these three steps and the structure of the proposed HiF-TSE model are presented in Fig.…”

Section: Hif-tse Modelmentioning

confidence: 99%

“…After determining the number of clusters, the authors aimed to ensure that the fuzzy clustering results were not affected by the sample data and that a stable fuzzy range was obtained. To achieve this goal, the bootstrap method was used to expand the sample size through data resampling (Banerjee and Monni, 2021). In statistics, the bootstrap method refers to resampling from the sample itself to infer the sample distribution.…”

Section: Fcm Clusteringmentioning

confidence: 99%

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Traffic state estimation incorporating heterogeneous vehicle composition: A high-dimensional fuzzy model

Wang,

Dong,

Shao

et al. 2024

Front. Eng. Manag.

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Accurate traffic state estimations (TSEs) within road networks are crucial for enhancing intelligent transportation systems and developing effective traffic management strategies. Traditional TSE methods often assume homogeneous traffic, where all vehicles are considered identical, which does not accurately reflect the complexities of real traffic conditions that often exhibit heterogeneous characteristics. In this study, we address the limitations of conventional models by introducing a novel TSE model designed for precise estimations of heterogeneous traffic flows. We develop a comprehensive traffic feature index system tailored for heterogeneous traffic that includes four elements: basic traffic parameters, heterogeneous vehicle speeds, heterogeneous vehicle flows, and mixed flow rates. This system aids in capturing the unique traffic characteristics of different vehicle types. Our proposed high-dimensional fuzzy TSE model, termed HiF-TSE, integrates three main processes: feature selection, which eliminates redundant traffic features using Spearman correlation coefficients; dimension reduction, which utilizes the T-distributed stochastic neighbor embedding machine learning algorithm to reduce high-dimensional traffic feature data; and FCM clustering, which applies the fuzzy C-means algorithm to classify the simplified data into distinct clusters. The HiF-TSE model significantly reduces computational demands and enhances efficiency in TSE processing. We validate our model through a real-world case study, demonstrating its ability to adapt to variations in vehicle type compositions within heterogeneous traffic and accurately represent the actual traffic state.

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Section: Hif-tse Modelmentioning

confidence: 99%

Section: Fcm Clusteringmentioning

confidence: 99%