Spatial-temporal aware inductive graph neural network for C-ITS data recovery
With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are
deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several …
deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several …
High-dimensional data analytics in civil engineering: A review on matrix and tensor decomposition
Recent developments in sensing and monitoring techniques have led to the generation of
high-dimensional data in the field of civil engineering. High-dimensional data analytics …
high-dimensional data in the field of civil engineering. High-dimensional data analytics …
ImputeFormer: Low rankness-induced transformers for generalizable spatiotemporal imputation
Missing data is a pervasive issue in both scientific and engineering tasks, especially for the
modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank …
modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank …
HRST-LR: a hessian regularization spatio-temporal low rank algorithm for traffic data imputation
Intelligent Transportation Systems (ITSs) are vital for alleviating traffic congestion and
improving traffic efficiency. Due to the delay of network transmission and failure of detectors …
improving traffic efficiency. Due to the delay of network transmission and failure of detectors …
Ginar: An end-to-end multivariate time series forecasting model suitable for variable missing
Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely
forecast the future values/trends, based on the complex relationships identified from …
forecast the future values/trends, based on the complex relationships identified from …
Blackout missing data recovery in industrial time series based on masked-former hierarchical imputation framework
In industrial processes, frequent communication failures and information corruption may
result in the loss of entire blocks of industrial process data, which is also known as blackout …
result in the loss of entire blocks of industrial process data, which is also known as blackout …
A comprehensive survey on traffic missing data imputation
Intelligent Transportation Systems (ITS) are essential and play a key role in improving road
safety, reducing congestion, optimizing traffic flow and facilitating the development of smart …
safety, reducing congestion, optimizing traffic flow and facilitating the development of smart …
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 …
have brought unprecedented amount of data to assist transportation engineers and …
Physics-informed deep learning for traffic state estimation: A survey and the outlook
For its robust predictive power (compared to pure physics-based models) and sample-
efficient training (compared to pure deep learning models), physics-informed deep learning …
efficient training (compared to pure deep learning models), physics-informed deep learning …
A nonlocal similarity learning-based tensor completion model with its application in intelligent transportation system
Predicting the traffic flow has been one of the most important applications in intelligent
transportation system. However, the missing information in the traffic data will directly affect …
transportation system. However, the missing information in the traffic data will directly affect …