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 with no code
Spatiotemporal Regularized Tucker Decomposition Approach for Traffic Data Imputation
Then, based on Tucker decomposition, the tensor is approximated by multiplying non-negative factor matrices with a sparse core tensor.
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks
Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems.
A Deep Learning Framework for Traffic Data Imputation Considering Spatiotemporal Dependencies
Spatiotemporal (ST) data collected by sensors can be represented as multi-variate time series, which is a sequence of data points listed in an order of time.
Large-Scale Traffic Data Imputation with Spatiotemporal Semantic Understanding
Specifically, the proposed model introduces semantic descriptions consisting of network-wide spatial and temporal information of traffic data to help the GT-TDI model capture spatiotemporal correlations at a network level.
Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns
The results show that our proposed model outperforms existing deep learning models in all kinds of missing scenarios and the graph structure estimation technique contributes to the model performance.
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images
This study shows that the proposed model can significantly improve the traffic data imputation accuracy in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to state-of-the-art models on the benchmark dataset.
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation
Traffic speed data imputation is a fundamental challenge for data-driven transport analysis.
