Deep learning for air pollutant concentration prediction: A review
Air pollution has become one of the critical environmental problem in the 21st century and
has attracted worldwide attentions. To mitigate it, many researchers have investigated the …
has attracted worldwide attentions. To mitigate it, many researchers have investigated the …
Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …
predict the future number of incoming and outgoing people in regions. Measuring the …
A hybrid dipper throated optimization algorithm and particle swarm optimization (DTPSO) model for hepatocellular carcinoma (HCC) prediction
Hepatocellular carcinoma (HCC) is a form of liver cancer that is widespread in Europe,
Africa, and Asia. The early identification of HCC is critical in improving the likelihood of …
Africa, and Asia. The early identification of HCC is critical in improving the likelihood of …
[HTML][HTML] How machine learning informs ride-hailing services: A survey
In recent years, online ride-hailing services have emerged as an important component of
urban transportation system, which not only provide significant ease for residents' travel …
urban transportation system, which not only provide significant ease for residents' travel …
Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks
For intelligent transportation systems (ITS), predicting urban traffic crowd flows is of great
importance. However, it is challenging to represent various complex spatial relationships …
importance. However, it is challenging to represent various complex spatial relationships …
A hybrid-convolution spatial–temporal recurrent network for traffic flow prediction
X Zhang, S Wen, L Yan, J Feng, Y Xia - The Computer Journal, 2024 - academic.oup.com
Accurate traffic flow prediction is valuable for satisfying citizens' travel needs and alleviating
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …
[PDF][PDF] LSGCN: Long short-term traffic prediction with graph convolutional networks.
Traffic prediction is a classical spatial-temporal prediction problem with many real-world
applications such as intelligent route planning, dynamic traffic management, and smart …
applications such as intelligent route planning, dynamic traffic management, and smart …
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Spatiotemporal forecasting has various applications in neuroscience, climate and
transportation domain. Traffic forecasting is one canonical example of such learning task …
transportation domain. Traffic forecasting is one canonical example of such learning task …
Adaptive graph convolutional recurrent network for traffic forecasting
Modeling complex spatial and temporal correlations in the correlated time series data is
indispensable for understanding the traffic dynamics and predicting the future status of an …
indispensable for understanding the traffic dynamics and predicting the future status of an …