Traffic Data Imputation
11 papers with code • 2 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Traffic Data Imputation
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Libraries
Use these libraries to find Traffic Data Imputation models and implementations
METR-LA Point Missing
Latest papers
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
WenjieDu/PyPOTS
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PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series, i. e. incomplete time series with missing values, A. K. A.
Laplacian Convolutional Representation for Traffic Time Series Imputation
In this study, we first introduce a Laplacian kernel to temporal regularization for characterizing local trends in traffic time series, which can be formulated in the form of circular convolution.
Traffic state data imputation: An efficient generating method based on the graph aggregator
Road traffic state estimation is an essential component of intelligent transportation systems (ITSs).
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
Graph-Machine-Learning-Group/spin
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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 propagation architecture aligned with the imputation task.
Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns
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.
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
torchspatiotemporal/tsl
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ICLR 2022
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 representations through message passing.
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation
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 $\times$ time of day $\times$ day) tensor.
Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
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
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
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information.
