High-Quality Representation Learning Approach to Spatio-Temporal Traffic Speed Data with Lp,ϵ-Norm

LFT model yields significantly higher imputation accuracy and superior computational efficiency compared with seven state-of-the-art approaches.

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Publication Details

Journal
Entropy
Published
2026-04-13
DOI
https://doi.org/10.3390/e28040435
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
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article

High-Quality Representation Learning Approach to Spatio-Temporal Traffic Speed Data with Lp,ϵ-Norm

Yikai Hou, Ziwen Ma
Entropy
Traffic Prediction and Management Techniques
article

High-Quality Representation Learning Approach to Spatio-Temporal Traffic Speed Data with Lp,ϵ-Norm

Yikai Hou, Ziwen Ma
article en

Abstract

LFT model yields significantly higher imputation accuracy and superior computational efficiency compared with seven state-of-the-art approaches.

EntropyVol. 28(4)
Chongqing University of Posts and Telecommunications (CN), Southwest University (CN)
Openalex Percentile: Top 25%
Traffic Prediction and Management Techniques
DOIOpen Access PDFOpenAlex
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High-Quality Representation Learning Approach to Spatio-Temporal Traffic Speed Data with Lp,ϵ-Norm — Yikai Hou, Ziwen Ma · Entropy (2026) | TGRS Research Map | TGRS