mlsanityData to 7 Oct 2026

Laplacian Convolutional Representation for Traffic Time Series Imputation

arXiv 2212.01529 ↗First posted 3 Dec 2022cs.LG cs.AIcited by 18

Not on the map. Why?

Abstract (arXiv)

Spatiotemporal traffic data imputation is of great significance in intelligent transportation systems and data-driven decision-making processes. To perform efficient learning and accurate reconstruction from partially observed traffic data, we assert the importance of characterizing both global and local trends in time series. In the literature, substantial works have demonstrated the effectiveness of utilizing the low-rank property of traffic data by matrix/tensor completion models. In this study, we first introduce a Laplacian kernel to temporal regularization for characterizing local trends in traffic time series, which can be formulated as a circular convolution. Then, we develop a low-rank Laplacian convolutional representation (LCR) model by putting the circulant matrix nuclear norm and the Laplacian kernelized temporal regularization together, which is proved to meet a unified framework that has a fast Fourier transform (FFT) solution in log-linear time complexity. Through extensive experiments on several traffic datasets, we demonstrate the superiority of LCR over several baseline models for imputing traffic time series of various time series behaviors (e.g., data noises and strong/weak periodicity) and reconstructing sparse speed fields of vehicular traffic flow. The proposed LCR model is also an efficient solution to large-scale traffic data imputation over the existing imputation models.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 14 of its 57 references that are in the map. First posted on arXiv 3 Dec 2022.

Builds on

14 of 57 references.

References · 14

  1. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
  2. 2018Tensor Robust Principal Component Analysis with A New Tensor Nuclear Normcited by 110
  3. 2020Inductive Graph Neural Networks for Spatiotemporal Krigingcited by 90
  4. 2019Bayesian Temporal Factorization for Multidimensional Time Series Predictioncited by 47
  5. 2022CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twinscited by 43
  6. 2021Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputationcited by 35
  7. 2020A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputationcited by 27
  8. 2013Tensor-based formulation and nuclear norm regularization for multi-energy computed tomographycited by 22
  9. 2021Low-Rank Hankel Tensor Completion for Traffic Speed Estimationcited by 22
  10. 2018Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Spacecited by 17
  11. 2020Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputationcited by 12
  12. 2019Recovery of Future Data via Convolution Nuclear Norm Minimizationcited by 11
2 more
  1. 2021Time Series Forecasting via Learning Convolutionally Low-Rank Modelscited by 9
  2. 2022Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorizationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 8; 2026: 3.

18 papers.

Sort:

By area:

  1. 2023ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputationcited by 50
  2. 2025Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Ratescited by 19
  3. 2024Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learnercited by 7
  4. 2024Accelerating Ill-conditioned Hankel Matrix Recovery via Structured Newton-like Descentcited by 3
  5. 2024Correlating Time Series with Interpretable Convolutional Kernelscited by 3
  6. 2024ODMixer: Fine-grained Spatial-temporal MLP for Metro Origin-Destination Predictioncited by 2
  7. 2025Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theorycited by 2
  8. 2025FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputationcited by 2
  9. 2024Graph Signal Adaptive Message Passingcited by 1
  10. 2026Spatial-Temporal Feedback Diffusion Guidance for Controlled Traffic Imputationcited by 1
  11. 2026Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinementcited by 1
  12. 2024A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Modelscited by 0
6 more
  1. 2025Collaborative Imputation of Urban Time Series through Cross-city Meta-learningcited by 0
  2. 2025Property Inheritance for Subtensors in Tensor Train Decompositionscited by 0
  3. 2025Robust Tensor Completion via Gradient Tensor Nulclear L1-L2 Norm for Traffic Data Recoverycited by 0
  4. 2025A Spatio-Temporal Online Robust Tensor Recovery Approach for Streaming Traffic Data Imputationcited by 0
  5. 2025Exact Recovery of Non-Random Missing Multidimensional Time Series via Temporal Isometric Delay-Embedding Transformcited by 0
  6. 2026A Multi-View Coupled Tensor Decomposition for Lightweight Online Adaptive Traffic Predictioncited by 0