Traffic Data Imputation

11 papers with code • 2 benchmarks • 2 datasets

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Use these libraries to find Traffic Data Imputation models and implementations
4 papers
2 papers

Most implemented papers

PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

WenjieDu/PyPOTS 30 May 2023

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.

BRITS: Bidirectional Recurrent Imputation for Time Series

caow13/BRITS NeurIPS 2018

It is ubiquitous that time series contains many missing values.

Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation

xinychen/transdim 7 Aug 2020

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.

Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks

Graph-Machine-Learning-Group/grin 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.

Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations

Graph-Machine-Learning-Group/spin 26 May 2022

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.

Traffic Data Imputation using Deep Convolutional Neural Networks

bilzinet/Traffic-state-reconstruction-using-Deep-CNN 21 Jan 2020

We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information.

A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation

xinychen/transdim 23 Mar 2020

Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems.

Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation

xinychen/transdim 30 Apr 2021

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.

Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns

tongnie/tensorlib 19 May 2022

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.