LAUSR

About LAUSR
Donate
Contact Us

Follow us:

You are not signed in:

Sign Up!


   Articles with "spatiotemporal graph" as a keyword



A multi-view bidirectional spatiotemporal graph network for urban traffic flow imputation

Sign Up to like & get
recommendations! Published in 2022 at "International Journal of Geographical Information Science"

DOI: 10.1080/13658816.2022.2032081

Abstract: Abstract Accurate estimation of missing traffic data is one of the essential components in intelligent transportation systems (ITS). The non-Euclidean data structure and complex missing traffic flow patterns make it challenging to capture nonlinear spatiotemporal… read more here.

Keywords: view; spatiotemporal graph; traffic; bidirectional spatiotemporal ... See more keywords

A Spatiotemporal Graph Attention Network Based on Synchronization for Epileptic Seizure Prediction

Sign Up to like & get
recommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"

DOI: 10.1109/jbhi.2022.3221211

Abstract: Accurate early prediction of epileptic seizures can provide timely treatment for patients. Previous studies have mainly focused on a single temporal or spatial dimension, making it difficult to take both relationships into account. Therefore, the… read more here.

Keywords: spatiotemporal graph; based synchronization; attention network; graph ... See more keywords

Remaining Useful Life Prediction Through Adaptive Spatiotemporal Graph Neural Network Based on Relationship Mining for Complex Aviation Equipment

Sign Up to like & get
recommendations! Published in 2025 at "IEEE Sensors Journal"

DOI: 10.1109/jsen.2025.3605144

Abstract: The evolution of the aviation industry has led to increased demands for enhanced reliability in aviation equipment, underscoring the need for reliable system operations, reduced production costs, and the prevention of unscheduled downtimes. Traditional maintenance… read more here.

Keywords: aviation equipment; aviation; complex aviation; equipment ... See more keywords

Spatiotemporal Graph Mixture of Experts for Highway Traffic Flow Prediction

Sign Up to like & get
recommendations! Published in 2025 at "IEEE Intelligent Transportation Systems Magazine"

DOI: 10.1109/mits.2025.3614710

Abstract: With excellent learning ability, the pretrained large model is challenging the mainstream traffic prediction paradigm. However, the pretraining process of large spatiotemporal models still faces the problems of high training cost and fixed graph size… read more here.

Keywords: mixture experts; prediction; graph mixture; spatiotemporal graph ... See more keywords

A Deep Learning-Based Feature Extraction and Knowledge Discovery Method for Spatiotemporal Graph Data

Sign Up to like & get
recommendations! Published in 2025 at "IEEE Transactions on Computational Social Systems"

DOI: 10.1109/tcss.2024.3501680

Abstract: With the rapid development of new technologies such as the Internet of Things and mobile Internet, human society can obtain a large number of spatiotemporal data such as time sequence sensing data and video data.… read more here.

Keywords: graph data; knowledge discovery; graph; spatiotemporal graph ... See more keywords

Accurate Oceanic Parameter Predictions via Spatiotemporal Graph Neural Network With Incremental Learning

Sign Up to like & get
recommendations! Published in 2025 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2025.3614734

Abstract: Accurate oceanic parameter predictions have occupied pivotal roles in the fields of ocean research, enabling to protection ocean environment, regulating global climate, and preventing extreme weather. However, the current observational techniques typically struggle to acquire… read more here.

Keywords: oceanic parameter; accurate oceanic; graph neural; parameter predictions ... See more keywords

Uncertainty Quantification for Traffic Forecasting Using Deep-Ensemble-Based Spatiotemporal Graph Neural Networks

Sign Up to like & get
recommendations! Published in 2024 at "IEEE Transactions on Intelligent Transportation Systems"

DOI: 10.1109/tits.2024.3381099

Abstract: Deep-learning-based data-driven forecasting methods have achieved impressive results for traffic forecasting. Specifically, spatiotemporal graph neural networks have emerged as a promising class of approaches because of their ability to model both spatial and temporal patterns… read more here.

Keywords: traffic forecasting; graph neural; uncertainty; spatiotemporal graph ... See more keywords

Synchronous Spatiotemporal Graph Transformer: A New Framework for Traffic Data Prediction.

Sign Up to like & get
recommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"

DOI: 10.1109/tnnls.2022.3169488

Abstract: Modeling the spatiotemporal relationship (STR) of traffic data is important yet challenging for existing graph networks. These methods usually capture features separately in temporal and spatial dimensions or represent the spatiotemporal data by adopting multiple… read more here.

Keywords: synchronous spatiotemporal; spatiotemporal graph; graph; traffic data ... See more keywords

Learning Spatiotemporal Graph Representations for Visual Perception Using EEG Signals

Sign Up to like & get
recommendations! Published in 2022 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"

DOI: 10.1109/tnsre.2022.3217344

Abstract: Perceiving and recognizing objects enable interaction with the external environment. Recently, decoding brain signals based on brain-computer interface (BCI) that recognize the user’s intentions by just looking at objects has attracted attention as a next-generation… read more here.

Keywords: neural network; visual perception; spatiotemporal graph; convolutional neural ... See more keywords

ASISTGCRN: A novel approach to traffic prediction using attention-based spatiotemporal graph networks

Sign Up to like & get
recommendations! Published in 2025 at "Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering"

DOI: 10.1177/09544070251390950

Abstract: Traffic flow forecasting is a fundamental task in intelligent transportation systems, directly supporting traffic control, congestion mitigation, and urban mobility planning. However, prediction remains difficult owing to nonlinear dynamics, rapidly changing spatiotemporal dependencies, and the… read more here.

Keywords: attention; prediction; spatiotemporal graph; attention based ... See more keywords

Residual-aware health prediction of power transformers via spatiotemporal graph neural networks

Sign Up to like & get
recommendations! Published in 2025 at "PLOS One"

DOI: 10.1371/journal.pone.0332381

Abstract: Accurate health state prediction and timely fault detection of power transformers are critical for ensuring the reliability and resilience of modern power systems. This paper proposes a residual-aware spatiotemporal graph neural network (STGNN) framework that… read more here.

Keywords: prediction; power transformers; spatiotemporal graph; residual aware ... See more keywords