Modeling dynamic spatio-temporal correlations for urban traffic flows prediction
Prediction of traffic crowd movement is one of the most important component in many
applications' domains ranging from urban management to transportation schedule. The key …
applications' domains ranging from urban management to transportation schedule. The key …
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 …
Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks
Predicting the accurate traffic crowd flows is of practical importance for intelligent
transportation systems (ITS). However, it is challenging because traffic flows are affected by …
transportation systems (ITS). However, it is challenging because traffic flows are affected by …
Densely connected convolutional networks with attention LSTM for crowd flows prediction
With the rapid progress of urbanization, predicting citywide crowd flows has become
increasingly significant in many fields, such as traffic management and public security …
increasingly significant in many fields, such as traffic management and public security …
Deep spatio-temporal neural network based on interactive attention for traffic flow prediction
H Zeng, Z Peng, XH Huang, Y Yang, R Hu - Applied Intelligence, 2022 - Springer
Traffic flow forecasting is of great significance to urban traffic control and public safety
applications. The key challenge of traffic flow forecasting is how to capture the complex …
applications. The key challenge of traffic flow forecasting is how to capture the complex …
Deep spatial-temporal networks for crowd flows prediction by dilated convolutions and region-shifting attention mechanism
C Tian, X Zhu, Z Hu, J Ma - Applied Intelligence, 2020 - Springer
Flow prediction at a citywide level is of great significance to traffic management and public
safety. Since deep learning has achieved success to deal with complex nonlinear problems …
safety. Since deep learning has achieved success to deal with complex nonlinear problems …
Spatio-temporal recurrent convolutional networks for citywide short-term crowd flows prediction
With the rapid development of urban traffic, forecasting the flows of crowd plays an
increasingly important role in traffic management and public safety. However, it is very …
increasingly important role in traffic management and public safety. However, it is very …
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 …
Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks
Traffic flow prediction is crucial for public safety and traffic management, and remains a big
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …
Context-aware spatial-temporal neural network for citywide crowd flow prediction via modeling long-range spatial dependency
Crowd flow prediction is of great importance in a wide range of applications from urban
planning, traffic control to public safety. It aims at predicting the inflow (the traffic of crowds …
planning, traffic control to public safety. It aims at predicting the inflow (the traffic of crowds …