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 …
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 …
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 …
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 …
[HTML][HTML] Predicting citywide crowd flows using deep spatio-temporal residual networks
Forecasting the flow of crowds is of great importance to traffic management and public
safety, and very challenging as it is affected by many complex factors, including spatial …
safety, and very challenging as it is affected by many complex factors, including spatial …
DeepSTD: Mining spatio-temporal disturbances of multiple context factors for citywide traffic flow prediction
Deep learning techniques have been widely applied to traffic flow prediction, considering
underlying routine patterns, and multiple context factors (eg, time and weather). However …
underlying routine patterns, and multiple context factors (eg, time and weather). However …
Deep spatio-temporal residual networks for citywide crowd flows prediction
Forecasting the flow of crowds is of great importance to traffic management and public
safety, and very challenging as it is affected by many complex factors, such as inter-region …
safety, and very challenging as it is affected by many complex factors, such as inter-region …
FASTNN: a deep learning approach for traffic flow prediction considering spatiotemporal features
Q Zhou, N Chen, S Lin - Sensors, 2022 - mdpi.com
Traffic flow forecasting is a critical input to intelligent transportation systems. Accurate traffic
flow forecasting can provide an effective reference for implementing traffic management …
flow forecasting can provide an effective reference for implementing traffic management …
MS-Net: Multi-source spatio-temporal network for traffic flow prediction
Predicting urban traffic flow is a challenging task, due to the complicated spatio-temporal
dependencies on traffic networks. Urban traffic flow usually has both short-term neighboring …
dependencies on traffic networks. Urban traffic flow usually has both short-term neighboring …