Gman: A graph multi-attention network for traffic prediction
Long-term traffic prediction is highly challenging due to the complexity of traffic systems and
the constantly changing nature of many impacting factors. In this paper, we focus on the …
the constantly changing nature of many impacting factors. In this paper, we focus on the …
A graph and attentive multi-path convolutional network for traffic prediction
Traffic prediction is an important and yet highly challenging problem due to the complexity
and constantly changing nature of traffic systems. To address the challenges, we propose a …
and constantly changing nature of traffic systems. To address the challenges, we propose a …
A trend graph attention network for traffic prediction
C Wang, R Tian, J Hu, Z Ma - Information Sciences, 2023 - Elsevier
Traffic prediction is an important part of urban computing. Accurate traffic prediction assists
the public in planning travel routes and relevant departments in traffic management, thus …
the public in planning travel routes and relevant departments in traffic management, thus …
Spatial‐temporal attention wavenet: A deep learning framework for traffic prediction considering spatial‐temporal dependencies
Traffic prediction on road networks is highly challenging due to the complexity of traffic
systems and is a crucial task in successful intelligent traffic system applications. Existing …
systems and is a crucial task in successful intelligent traffic system applications. Existing …
Hierarchical graph convolution network for traffic forecasting
Traffic forecasting is attracting considerable interest due to its widespread application in
intelligent transportation systems. Given the complex and dynamic traffic data, many …
intelligent transportation systems. Given the complex and dynamic traffic data, many …
Multi-stage attention spatial-temporal graph networks for traffic prediction
Accurate traffic prediction plays an important role in Intelligent Transportation System. This
problem is very challenging due to the heterogeneity and dynamic spatio-temporal …
problem is very challenging due to the heterogeneity and dynamic spatio-temporal …
Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due
to the time-varying traffic patterns and the complicated spatial dependencies on road …
to the time-varying traffic patterns and the complicated spatial dependencies on road …
Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting
Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent
transportation systems. Despite years of studies, accurate traffic prediction still faces the …
transportation systems. Despite years of studies, accurate traffic prediction still faces the …
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 …
[PDF][PDF] LSGCN: Long short-term traffic prediction with graph convolutional networks.
Traffic prediction is a classical spatial-temporal prediction problem with many real-world
applications such as intelligent route planning, dynamic traffic management, and smart …
applications such as intelligent route planning, dynamic traffic management, and smart …