Graph neural network for traffic forecasting: The research progress
Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
GNN-based long and short term preference modeling for next-location prediction
Next-location prediction is a special task of the next POIs recommendation. Different from
general recommendation tasks, next-location prediction is highly context-dependent:(1) …
general recommendation tasks, next-location prediction is highly context-dependent:(1) …
A decomposition dynamic graph convolutional recurrent network for traffic forecasting
Our daily lives are greatly impacted by traffic conditions, making it essential to have accurate
predictions of traffic flow within a road network. Traffic signals used for forecasting are …
predictions of traffic flow within a road network. Traffic signals used for forecasting are …
Transformer-enhanced periodic temporal convolution network for long short-term traffic flow forecasting
Q Ren, Y Li, Y Liu - Expert Systems with Applications, 2023 - Elsevier
Abstract Recently, Temporal Convolution Networks (TCNs) and Graph Convolution Network
(GCN) have been developed for traffic forecasting and obtained promising results as their …
(GCN) have been developed for traffic forecasting and obtained promising results as their …
[HTML][HTML] Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping
Perceiving the future trend of Vessel Traffic Flow (VTF) in advance has great application
values in the maritime industry. However, using such big data from the Automatic …
values in the maritime industry. However, using such big data from the Automatic …
Enhancing time series forecasting: a hierarchical transformer with probabilistic decomposition representation
J Tong, L Xie, W Yang, K Zhang, J Zhao - Information Sciences, 2023 - Elsevier
Time series forecasting is crucial for several fields, such as disaster warning, weather
prediction, and energy consumption. Transformer-based models are considered to have …
prediction, and energy consumption. Transformer-based models are considered to have …
Urban regional function guided traffic flow prediction
The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis,
which has recently gained increasing interest. In addition to spatial-temporal correlations …
which has recently gained increasing interest. In addition to spatial-temporal correlations …
Discrete log anomaly detection: a novel time-aware graph-based link prediction approach
L Yan, C Luo, R Shao - Information Sciences, 2023 - Elsevier
With the implementation of online-service information systems, it is important to detect
system anomalies. Logs, serving as the system runtime information, are the key resources to …
system anomalies. Logs, serving as the system runtime information, are the key resources to …
PKET-GCN: prior knowledge enhanced time-varying graph convolution network for traffic flow prediction
Due to prediction on the traffic flow is influenced by the real environment and historical data,
the produced traffic graph may include significant uncertainty. The graph convolution …
the produced traffic graph may include significant uncertainty. The graph convolution …
[HTML][HTML] A hierarchical methodology for vessel traffic flow prediction using Bayesian tensor decomposition and similarity grouping
Accurate vessel traffic flow (VTF) prediction can enhance navigation safety and economic
efficiency. To address the challenge of the inherently complex and dynamic growth of the …
efficiency. To address the challenge of the inherently complex and dynamic growth of the …