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 …
Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction
As a core technology of Intelligent Transportation System, traffic flow prediction has a wide
range of applications. The fundamental challenge in traffic flow prediction is to effectively …
range of applications. The fundamental challenge in traffic flow prediction is to effectively …
Predicting citywide road traffic flow using deep spatiotemporal neural networks
Traffic flow forecasting has been a long-standing topic in intelligent transportation systems,
and a renewed interest has been seen in recent years due to the development of artificial …
and a renewed interest has been seen in recent years due to the development of artificial …
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 …
Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting
S Lan, Y Ma, W Huang, W Wang… - … on machine learning, 2022 - proceedings.mlr.press
As a typical problem in time series analysis, traffic flow prediction is one of the most
important application fields of machine learning. However, achieving highly accurate traffic …
important application fields of machine learning. However, achieving highly accurate traffic …
Temporal multi-graph convolutional network for traffic flow prediction
M Lv, Z Hong, L Chen, T Chen… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Traffic flow prediction plays an important role in ITS (Intelligent Transportation System). This
task is challenging due to the complex spatial and temporal correlations (eg, the constraints …
task is challenging due to the complex spatial and temporal correlations (eg, the constraints …
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 …
3d graph convolutional networks with temporal graphs: A spatial information free framework for traffic forecasting
Spatio-temporal prediction plays an important role in many application areas especially in
traffic domain. However, due to complicated spatio-temporal dependency and high non …
traffic domain. However, due to complicated spatio-temporal dependency and high non …
Multi-view dynamic graph convolution neural network for traffic flow prediction
The rapid urbanization and continuous improvement of road traffic equipment result in
massive daily production of traffic data. These data contain the long-term evolution of traffic …
massive daily production of traffic data. These data contain the long-term evolution of traffic …
Hierarchical traffic flow prediction based on spatial-temporal graph convolutional network
In recent years, traffic flow prediction has attracted more and more interest from both
academia and industry since such information can provide effective guidance for traffic …
academia and industry since such information can provide effective guidance for traffic …