A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing
Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
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 …
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 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 …
[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 …
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 …
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 …
Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis
Crowd flow prediction is of great importance in a wide range of applications from urban
planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds …
planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds …
Urban flow prediction with spatial–temporal neural ODEs
With the recent advances in deep learning, data-driven methods have shown compelling
performance in various application domains enabling the Smart Cities paradigm …
performance in various application domains enabling the Smart Cities paradigm …
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 …