Graph neural network: A comprehensive review on non-euclidean space
This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …
based networks from a deep learning perspective. Graph networks provide a generalized …
How to build a graph-based deep learning architecture in traffic domain: A survey
In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
Omg: Towards effective graph classification against label noise
Graph classification is a fundamental problem with diverse applications in bioinformatics
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …
Towards mobility data science (vision paper)
Mobility data captures the locations of moving objects such as humans, animals, and cars.
With the availability of GPS-equipped mobile devices and other inexpensive location …
With the availability of GPS-equipped mobile devices and other inexpensive location …
Anomaly detection in time series with robust variational quasi-recurrent autoencoders
We propose variational quasi-recurrent autoencoders (VQRAEs) to enable robust and
efficient anomaly detection in time series in unsupervised settings. The proposed VQRAEs …
efficient anomaly detection in time series in unsupervised settings. The proposed VQRAEs …
Semi-supervised air quality forecasting via self-supervised hierarchical graph neural network
Predicting air quality in fine spatiotemporal granularity is of great importance for air pollution
control and urban sustainability. However, existing studies are either focused on predicting …
control and urban sustainability. However, existing studies are either focused on predicting …
STGNN-TTE: Travel time estimation via spatial–temporal graph neural network
Estimating the travel time of urban trajectories is a basic but challenging task in many
intelligent transportation systems, which is the foundation of route planning and traffic …
intelligent transportation systems, which is the foundation of route planning and traffic …
Traffic-GGNN: predicting traffic flow via attentional spatial-temporal gated graph neural networks
Y Wang, J Zheng, Y Du, C Huang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Recent spatial-temporal graph-based deep learning methods for Traffic Flow Prediction
(TFP) problems have shown superior performance in modeling higher-level spatial …
(TFP) problems have shown superior performance in modeling higher-level spatial …
Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks
Origin-destination (OD) matrices are used widely in transportation and logistics to record the
travel cost (eg, travel speed or greenhouse gas emission) between pairs of OD regions …
travel cost (eg, travel speed or greenhouse gas emission) between pairs of OD regions …
A deep learning approach for aircraft trajectory prediction in terminal airspace
Current state-of-the-art trajectory methods do not perform well in the terminal airspace that
surrounds an airport due to its complex airspace structure and the frequently changing flight …
surrounds an airport due to its complex airspace structure and the frequently changing flight …