Graph neural network for traffic forecasting: The research progress
Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
[HTML][HTML] AGNP: Network-wide short-term probabilistic traffic speed prediction and imputation
Abstract The data-driven Intelligent Transportation System (ITS) provides great support to
travel decisions and system management but inevitably encounters the issue of data …
travel decisions and system management but inevitably encounters the issue of data …
Dynamic spatio-temporal graph network with adaptive propagation mechanism for multivariate time series forecasting
ZL Li, J Yu, GW Zhang, LY Xu - Expert Systems with Applications, 2023 - Elsevier
Spatio-temporal prediction on multivariate time series has received tremendous attention for
extensive applications in the real world, where the dynamic unknown spatio-temporal …
extensive applications in the real world, where the dynamic unknown spatio-temporal …
Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction
Deep learning models have emerged as a promising way for traffic prediction. However, the
requirement for large amounts of training data remains a significant issue for achieving well …
requirement for large amounts of training data remains a significant issue for achieving well …
ST-DAGCN: A spatiotemporal dual adaptive graph convolutional network model for traffic prediction
Accurately predicting traffic flow characteristics is crucial for effective urban transportation
management. Emergence of artificial intelligence has led to the surge of deep learning …
management. Emergence of artificial intelligence has led to the surge of deep learning …
[HTML][HTML] RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction
Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
KSTAGE: A knowledge-guided spatial-temporal attention graph learning network for crop yield prediction
M Qiao, X He, X Cheng, P Li, Q Zhao, C Zhao… - Information Sciences, 2023 - Elsevier
Accurate and timely crop yield prediction is difficult to achieve due to the nonlinear and
dynamic spatial–temporal correlations included during the crop growth process. The latest …
dynamic spatial–temporal correlations included during the crop growth process. The latest …
Hybrid deep learning and quantum-inspired neural network for day-ahead spatiotemporal wind speed forecasting
Wind is an essential, clean and sustainable renewable source of energy; however, wind
speed is stochastic and intermittent. Accurate wind power generation forecasts are required …
speed is stochastic and intermittent. Accurate wind power generation forecasts are required …
Road traffic flow prediction based on dynamic spatiotemporal graph attention network
Y Chen, J Huang, H Xu, J Guo, L Su - Scientific reports, 2023 - nature.com
To improve the prediction accuracy of traffic flow under the influence of nearby time traffic
flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction …
flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction …
Dynamic multi-graph neural network for traffic flow prediction incorporating traffic accidents
Y Ye, Y **ao, Y Zhou, S Li, Y Zang, Y Zhang - Expert Systems with …, 2023 - Elsevier
Traffic flow forecasting is the foundation of intelligent transportation development and an
important task in realizing intelligent transportation services. This task is challenging due to …
important task in realizing intelligent transportation services. This task is challenging due to …