Traffic Data Imputation
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Benchmarks
METR-LA Point Missing
PEMS-BAY Point Missing
Most implemented
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
BRITS: Bidirectional Recurrent Imputation for Time Series
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
Papers
A Spatio-Temporal Online Robust Tensor Recovery Approach for Streaming Traffic Data Imputation
Data quality is critical to Intelligent Transportation Systems (ITS), as complete and accurate traffic data underpin reliable decision-making in traffic control and management. Recent advances in low-rank tensor recovery…
Computational EfficiencyTraffic Data ImputationLRTuckerRep: Low-rank Tucker Representation Model for Multi-dimensional Data Completion
Multi-dimensional data completion is a critical problem in computational sciences, particularly in domains such as computer vision, signal processing, and scientific computing. Existing methods typically leverage either …
Traffic Data ImputationImage InpaintingSTAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
Traffic data imputation is fundamentally important to support various applications in intelligent transportation systems such as traffic flow prediction. However, existing time-to-space sequential methods often fail to e…
Graph AttentionImputationMixture-of-ExpertsTraffic Data ImputationMNT-TNN: Spatiotemporal Traffic Data Imputation via Compact Multimode Nonlinear Transform-based Tensor Nuclear Norm
Imputation of random or non-random missing data is a long-standing research topic and a crucial application for Intelligent Transportation Systems (ITS). However, with the advent of modern communication technologies such…
ImputationMissing ValuesTraffic Data ImputationAn Experimental Evaluation of Imputation Models for Spatial-Temporal Traffic Data
Traffic data imputation is a critical preprocessing step in intelligent transportation systems, enabling advanced transportation services. Despite significant advancements in this field, selecting the most suitable model…
BenchmarkingImputationTraffic Data ImputationDiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data
The application of reinforcement learning in traffic signal control (TSC) has been extensively researched and yielded notable achievements. However, most existing works for TSC assume that traffic data from all surroundi…
Decision MakingImputationTraffic Data ImputationTraffic Signal Control