Spatial-temporal fusion graph neural networks for traffic flow forecasting
Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated
spatial dependencies and dynamical trends of temporal pattern between different roads …
spatial dependencies and dynamical trends of temporal pattern between different roads …
Deep learning on traffic prediction: Methods, analysis, and future directions
X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
FC-GAGA: Fully connected gated graph architecture for spatio-temporal traffic forecasting
BN Oreshkin, A Amini, L Coyle, M Coates - Proceedings of the AAAI …, 2021 - ojs.aaai.org
Forecasting of multivariate time-series is an important problem that has applications in traffic
management, cellular network configuration, and quantitative finance. A special case of the …
management, cellular network configuration, and quantitative finance. A special case of the …
Multi‐graph convolutional network for short‐term passenger flow forecasting in urban rail transit
Short‐term passenger flow forecasting is a crucial task for urban rail transit operations.
Emerging deep‐learning technologies have become effective methods used to overcome …
Emerging deep‐learning technologies have become effective methods used to overcome …
STGAT: Spatial-temporal graph attention networks for traffic flow forecasting
Traffic flow forecasting is a critical task for urban traffic control and dispatch in the field of
transportation, which is characterized by the high nonlinearity and complexity. In this paper …
transportation, which is characterized by the high nonlinearity and complexity. In this paper …
Detecting and mitigating DDoS attacks in SDN using spatial-temporal graph convolutional network
Y Cao, H Jiang, Y Deng, J Wu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
With the development of data plane programmable Software-Defined Networking (SDN),
Distributed Denial of Service (DDoS) attacks on the data plane increasingly become fatal …
Distributed Denial of Service (DDoS) attacks on the data plane increasingly become fatal …
A deep learning approach for flight delay prediction through time-evolving graphs
Flight delay prediction has recently gained growing popularity due to the significant role it
plays in efficient airline and airport operation. Most of the previous prediction works consider …
plays in efficient airline and airport operation. Most of the previous prediction works consider …
STP-TrellisNets+: Spatial-temporal parallel TrellisNets for multi-step metro station passenger flow prediction
The drastic increase of metro passengers in recent years inevitably causes the
overcrowdedness in the metro systems. Accurately predicting passenger flows at metro …
overcrowdedness in the metro systems. Accurately predicting passenger flows at metro …
Unified spatio-temporal modeling for traffic forecasting using graph neural network
Research in deep learning models to forecast traffic intensities has gained great attention in
recent years due to their capability to capture the complex spatio-temporal relationships …
recent years due to their capability to capture the complex spatio-temporal relationships …
Spatial–temporal tensor graph convolutional network for traffic speed prediction
Accurate traffic speed prediction is crucial for the guidance and management of urban traffic,
which at the same time requires a model with a satisfactory computational burden and …
which at the same time requires a model with a satisfactory computational burden and …