[HTML][HTML] 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 …
Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks
Y Chengqing, Y Guangxi, Y Chengming, Z Yu, M Xiwei - Energy, 2023 - Elsevier
Spatiotemporal wind power prediction technology could provide technical support for wind
farm energy regulation and dynamic planning. In the paper, a novel ensemble deep graph …
farm energy regulation and dynamic planning. In the paper, a novel ensemble deep graph …
A novel AQI forecasting method based on fusing temporal correlation forecasting with spatial correlation forecasting
Air is an essential natural resource, and the Air Quality Index (AQI) is an important indicator
visually reflecting air quality. Accurate AQI prediction is critical for controlling air pollution …
visually reflecting air quality. Accurate AQI prediction is critical for controlling air pollution …
Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China
The spatio-temporal wind speed prediction technology provides the key technical support for
the energy management and space allocation of the wind farm. To obtain an accurate spatio …
the energy management and space allocation of the wind farm. To obtain an accurate spatio …
A dynamic ensemble deep deterministic policy gradient recursive network for spatiotemporal traffic speed forecasting in an urban road network
Traffic congestion is a difficult problem that restricts the construction of urbanization.
Spatiotemporal traffic speed forecasting technologies can provide effective technical support …
Spatiotemporal traffic speed forecasting technologies can provide effective technical support …
Ensemble reinforcement learning: A survey
Reinforcement Learning (RL) has emerged as a highly effective technique for addressing
various scientific and applied problems. Despite its success, certain complex tasks remain …
various scientific and applied problems. Despite its success, certain complex tasks remain …
Inferring intercity freeway truck volume from the perspective of the potential destination city attractiveness
Accurately inferring the spatiotemporal distribution of freeway traffic volume is one of the
bottleneck problems for intelligent management of ground transportation. Although the …
bottleneck problems for intelligent management of ground transportation. Although the …
Time-variant post-processing method for long-term numerical wind speed forecasts based on multi-region recurrent graph network
Abstract Weather Research and Forecasting (WRF) is widely used for long-term wind speed
prediction. To reduce the inherent systematic error of the WRF, a graph-based wind speed …
prediction. To reduce the inherent systematic error of the WRF, a graph-based wind speed …
A new multipredictor ensemble decision framework based on deep reinforcement learning for regional gdp prediction
Q Li, C Yu, G Yan - IEEE Access, 2022 - ieeexplore.ieee.org
Gross domestic product (GDP) can effectively reflect the situation of economic development
and resource allocation in different regions. The high-precision GDP prediction technology …
and resource allocation in different regions. The high-precision GDP prediction technology …