Topological graph convolutional network-based urban traffic flow and density prediction
With the development of modern Intelligent Transportation System (ITS), reliable and
efficient transportation information sharing becomes more and more important. Although …
efficient transportation information sharing becomes more and more important. Although …
3dgcn: 3-dimensional dynamic graph convolutional network for citywide crowd flow prediction
Crowd flow prediction is an essential task benefiting a wide range of applications for the
transportation system and public safety. However, it is a challenging problem due to the …
transportation system and public safety. However, it is a challenging problem due to the …
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 …
Dynamic traffic correlations based spatio-temporal graph convolutional network for urban traffic prediction
Accurate urban traffic prediction is a critical issue in Intelligent Transportation Systems (ITS).
It is challenging since urban traffic usually indicates high dynamic spatio-temporal …
It is challenging since urban traffic usually indicates high dynamic spatio-temporal …
Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as
subway/bus stations, is a practical application and of great significance for urban traffic …
subway/bus stations, is a practical application and of great significance for urban traffic …
A general traffic flow prediction approach based on spatial-temporal graph attention
C Tang, J Sun, Y Sun, M Peng, N Gan - IEEE Access, 2020 - ieeexplore.ieee.org
Accurate and reliable traffic flow prediction is critical to the safe and stable deployment of
intelligent transportation systems. However, it is very challenging since the complex spatial …
intelligent transportation systems. However, it is very challenging since the complex spatial …
City-wide traffic flow forecasting using a deep convolutional neural network
S Sun, H Wu, L Xiang - Sensors, 2020 - mdpi.com
City-wide traffic flow forecasting is a significant function of the Intelligent Transport System
(ITS), which plays an important role in city traffic management and public travel safety …
(ITS), which plays an important role in city traffic management and public travel safety …
Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks
Being able to predict the crowd flows in each and every part of a city, especially in irregular
regions, is strategically important for traffic control, risk assessment, and public safety …
regions, is strategically important for traffic control, risk assessment, and public safety …
Hybrid spatio-temporal graph convolutional network: Improving traffic prediction with navigation data
R Dai, S Xu, Q Gu, C Ji, K Liu - Proceedings of the 26th acm sigkdd …, 2020 - dl.acm.org
Traffic forecasting has recently attracted increasing interest due to the popularity of online
navigation services, ridesharing and smart city projects. Owing to the non-stationary nature …
navigation services, ridesharing and smart city projects. Owing to the non-stationary nature …
Traffic flow prediction via spatial temporal graph neural network
Traffic flow analysis, prediction and management are keystones for building smart cities in
the new era. With the help of deep neural networks and big traffic data, we can better …
the new era. With the help of deep neural networks and big traffic data, we can better …