Traffic Data Imputation

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

14 shown of 14 papers with code (23 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
  30 May 2023 5 repositories listed Syntology ran 0 of 21 samples · 21 unverified
  PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series, i.
• BRITS: Bidirectional Recurrent Imputation for Time Series
  27 May 2018 5 repositories listed Syntology ran 2 of 4 samples · 2 unverified
  It is ubiquitous that time series contains many missing values.
• Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
  26 May 2022 2 repositories listed
  In particular, we propose a novel class of attention-based architectures that, given a set of highly sparse discrete observations, learn a representation for points in time and space by exploiting a spatiotemporal…
• Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
  31 Jul 2021 2 repositories listed Syntology ran 5 of 10 samples · 5 unverified · 9 pointer-only (licence)
  In particular, we introduce a novel graph neural network architecture, named GRIN, which aims at reconstructing missing data in the different channels of a multivariate time series by learning spatio-temporal…
• STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
  9 Jun 2025 1 repository listed
  Specifically, we introduce a Mixture of Experts (MoE) framework to capture latent spatio-temporal features and their influence weights, effectively imputing block missing.
• An Experimental Evaluation of Imputation Models for Spatial-Temporal Traffic Data
  6 Dec 2024 1 repository listed
  Traffic data imputation is a critical preprocessing step in intelligent transportation systems, enabling advanced transportation services.
• DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data
  30 Oct 2024 1 repository listed Syntology ran 0 of 4 samples · 4 unverified · 4 pointer-only (licence)
  The application of reinforcement learning in traffic signal control (TSC) has been extensively researched and yielded notable achievements.
• Laplacian Convolutional Representation for Traffic Time Series Imputation
  3 Dec 2022 1 repository listed
  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.
• Traffic state data imputation: An efficient generating method based on the graph aggregator
  12 Aug 2022 1 repository listed
  Road traffic state estimation is an essential component of intelligent transportation systems (ITSs).
• Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns
  19 May 2022 1 repository listed
  Rapid advances in sensor, wireless communication, cloud computing and data science have brought unprecedented amount of data to assist transportation engineers and researchers in making better decisions.
• Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation
  30 Apr 2021 1 repository listed
  In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework by introducing \textit{temporal variation} as a new regularization term into the completion of a third-order (sensor × time of day ×…
• Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
  7 Aug 2020 1 repository listed
  Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data.
• A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation
  23 Mar 2020 1 repository listed
  Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems.
• Traffic Data Imputation using Deep Convolutional Neural Networks
  21 Jan 2020 1 repository listed
  We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.