[HTML][HTML] RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction
Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
Discrete-time dynamic graph echo state networks
A Micheli, D Tortorella - Neurocomputing, 2022 - Elsevier
Relations between entities evolving through discrete time-steps can be represented by
discrete-time dynamic graphs. Examples include hourly interactions between social network …
discrete-time dynamic graphs. Examples include hourly interactions between social network …
Temporal network embedding framework with causal anonymous walks representations
I Makarov, A Savchenko, A Korovko, L Sherstyuk… - PeerJ Computer …, 2022 - peerj.com
Many tasks in graph machine learning, such as link prediction and node classification, are
typically solved using representation learning. Each node or edge in the network is encoded …
typically solved using representation learning. Each node or edge in the network is encoded …
Self-attention eidetic 3D-LSTM: Video prediction models for traffic flow forecasting
Video prediction is extremely challenging in a traffic flow forecasting problem due to
dynamic spatiotemporal dependence. Eidetic 3D convolutional long short-term memory …
dynamic spatiotemporal dependence. Eidetic 3D convolutional long short-term memory …
A Bayesian deep learning method for freeway incident detection with uncertainty quantification
Incident detection is fundamental for freeway management to reduce non-recurrent
congestions and secondary incidents. Recently, machine learning technologies have made …
congestions and secondary incidents. Recently, machine learning technologies have made …
Spatial–temporal tensor graph convolutional network for traffic speed prediction
Accurate traffic speed prediction is crucial for the guidance and management of urban traffic,
which at the same time requires a model with a satisfactory computational burden and …
which at the same time requires a model with a satisfactory computational burden and …
LSTTN: A Long-Short Term Transformer-based spatiotemporal neural network for traffic flow forecasting
Q Luo, S He, X Han, Y Wang, H Li - Knowledge-Based Systems, 2024 - Elsevier
Accurate traffic forecasting is a fundamental problem in intelligent transportation systems
and learning long-range traffic representations with key information through spatiotemporal …
and learning long-range traffic representations with key information through spatiotemporal …
STGHTN: Spatial-temporal gated hybrid transformer network for traffic flow forecasting
J Liu, Y Kang, H Li, H Wang, X Yang - Applied Intelligence, 2023 - Springer
Accurate traffic forecasting is a critical function of intelligent transportation systems, which
remains challenging due to the complex spatial and temporal dependence of traffic data …
remains challenging due to the complex spatial and temporal dependence of traffic data …
Stream temperature prediction in a shifting environment: Explaining the influence of deep learning architecture
Stream temperature is a fundamental control on ecosystem health. Recent efforts
incorporating process guidance into deep learning models for predicting stream temperature …
incorporating process guidance into deep learning models for predicting stream temperature …
STGC-GNNs: A GNN-based traffic prediction framework with a spatial–temporal Granger causality graph
S He, Q Luo, R Du, L Zhao, G He, H Fu, H Li - Physica A: Statistical …, 2023 - Elsevier
Accurate representation of the temporal dynamics of traffic flow traveling in the road network
is the key to traffic prediction, it is therefore important to model the spatial dependence of the …
is the key to traffic prediction, it is therefore important to model the spatial dependence of the …