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Found 5 papers, 5 papers with code
ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation
The exploitation of the inherent structures of spatiotemporal data enables our model to learn balanced signal-noise representations, making it versatile for a variety of imputation problems.
Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale
Spatiotemporal urban data (STUD) displays complex correlational patterns.
Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs
Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control.
Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach
In addition, sensors are prone to error or missing data due to various kinds of reasons, speeds from these sensors can become highly noisy.
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.
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