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Bike sharing usage prediction with deep learning: a survey
As a representative of shared mobility, bike sharing has become a green and convenient
way to travel in cities in recent years. Bike usage prediction becomes more important for …
way to travel in cities in recent years. Bike usage prediction becomes more important for …
A flow feedback traffic prediction based on visual quantified features
Traffic flow prediction methods commonly rely on historical traffic data, such as traffic volume
and speed, but may not be suitable for high-capacity expressways or during peak traffic …
and speed, but may not be suitable for high-capacity expressways or during peak traffic …
[HTML][HTML] In-depth insights into the application of recurrent neural networks (rnns) in traffic prediction: A comprehensive review
Traffic prediction is crucial for transportation management and user convenience. With the
rapid development of deep learning techniques, numerous models have emerged for traffic …
rapid development of deep learning techniques, numerous models have emerged for traffic …
STFGCN: Spatial–temporal fusion graph convolutional network for traffic prediction
Accurate traffic prediction plays a crucial role in improving traffic conditions and optimizing
road utilization. Effectively capturing the multi-scale temporal dependencies and dynamic …
road utilization. Effectively capturing the multi-scale temporal dependencies and dynamic …
A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings
The development of the building energy management systems (BEMS) enable users to
intelligently control Heating, Ventilation, Air-conditioning and Cooling (HVAC) systems …
intelligently control Heating, Ventilation, Air-conditioning and Cooling (HVAC) systems …
GraphSAGE-based dynamic spatial–temporal graph convolutional network for traffic prediction
T Liu, A Jiang, J Zhou, M Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing
such dependencies is critical to improving prediction accuracy. Recently, many deep …
such dependencies is critical to improving prediction accuracy. Recently, many deep …
Traffic-aware lightweight hierarchical offloading towards adaptive slicing-enabled sagin
The emerging Space-Air-Ground Integrated Networks (SAGIN) empower Mobile Edge
Computing (MEC) with wider communication coverage and more flexible network access …
Computing (MEC) with wider communication coverage and more flexible network access …
Interpretable local flow attention for multi-step traffic flow prediction
Traffic flow prediction (TFP) has attracted increasing attention with the development of smart
city. In the past few years, neural network-based methods have shown impressive …
city. In the past few years, neural network-based methods have shown impressive …
Multi-graph convolutional-recurrent neural network (MGC-RNN) for short-term forecasting of transit passenger flow
Short-term forecasting of passenger flow is critical for transit management and crowd
regulation. Spatial dependencies, temporal dependencies, inter-station correlations driven …
regulation. Spatial dependencies, temporal dependencies, inter-station correlations driven …
Short-term pv power forecasting based on ceemdan and ensemble deeptcn
Y Huang, A Wang, J Jiao, J **e… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the high percentage of access to photovoltaic (PV) power generation, accurate and
stable short-term PV power generation forecasting has become popular with the existing …
stable short-term PV power generation forecasting has become popular with the existing …