Papers Traffic Data Imputation
“Traffic Data Imputation” 태그가 달린 논문 25편 · 필터 해제
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 ControlFastSTI: A Fast Conditional Pseudo Numerical Diffusion Model for Spatio-temporal Traffic Data Imputation
High-quality spatiotemporal traffic data is crucial for intelligent transportation systems (ITS) and their data-driven applications. Inevitably, the issue of missing data caused by various disturbances threatens the reli…
DenoisingImputationTraffic Data ImputationPyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
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. irregularlysampled time series. Partic…
Anomaly DetectionClassificationClassification on Time Series with Missing DataClustering+12Spatiotemporal Regularized Tucker Decomposition Approach for Traffic Data Imputation
In intelligent transportation systems, traffic data imputation, estimating the missing value from partially observed data is an inevitable and challenging task. Previous studies have not fully considered traffic data's m…
ImputationTensor DecompositionTraffic Data ImputationST-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. However, real-world transportation data, collected from loop detectors or similar sources, often…
Deep LearningGraph AttentionImputationMissing Values+3A 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. Despite the vast amount of useful information, the ST data usu…
ImputationTime SeriesTraffic Data ImputationLarge-Scale Traffic Data Imputation with Spatiotemporal Semantic Understanding
Large-scale data missing is a challenging problem in Intelligent Transportation Systems (ITS). Many studies have been carried out to impute large-scale traffic data by considering their spatiotemporal correlations at a n…
ImputationTraffic Data ImputationLaplacian Convolutional Representation for Traffic Time Series Imputation
Spatiotemporal traffic data imputation is of great significance in intelligent transportation systems and data-driven decision-making processes. To perform efficient learning and accurate reconstruction from partially ob…
Decision MakingImage InpaintingImputationTime Series+2Traffic 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). However, road traffic state data collected by traffic detectors are often incomplete, which can cause problems acro…
Generative Adversarial NetworkImputationState EstimationTraffic Data ImputationLearning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
Modeling multivariate time series as temporal signals over a (possibly dynamic) graph is an effective representational framework that allows for developing models for time series analysis. In fact, discrete sequences of …
ImputationMultivariate Time Series ImputationTime SeriesTime Series Analysis+1Truncated 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. However, traffic…
Cloud ComputingImputationTraffic Data ImputationDynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns
Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent transportation systems. Despite extensive research regarding traffic data imputation, there still exist two limitations to b…
Deep LearningImputationTraffic Data ImputationFilling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
Dealing with missing values and incomplete time series is a labor-intensive, tedious, inevitable task when handling data coming from real-world applications. Effective spatio-temporal representations would allow imputati…
Graph Neural NetworkImputationMissing ValuesMultivariate Time Series Imputation+3Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation
Spatiotemporal traffic time series (e.g., traffic volume/speed) collected from sensing systems are often incomplete with considerable corruption and large amounts of missing values, preventing users from harnessing the f…
ImputationMissing ValuesTime SeriesTime Series Analysis+1Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
Missing value problem in spatiotemporal traffic data has long been a challenging topic, in particular for large-scale and high-dimensional data with complex missing mechanisms and diverse degrees of missingness. Recent s…
ImputationTraffic Data Imputation