Semantics-aware dynamic graph convolutional network for traffic flow forecasting
Traffic flow forecasting is a challenging task due to its spatio-temporal nature and the
stochastic features underlying complex traffic situations. Currently, Graph Convolutional …
stochastic features underlying complex traffic situations. Currently, Graph Convolutional …
Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and …
The deterministic and probabilistic prediction of ship motion is important for safe navigation
and stable real-time operational control of ships at sea. However, the volatility and …
and stable real-time operational control of ships at sea. However, the volatility and …
The use of teaching-learning based optimization technique for optimizing weld bead geometry as well as power consumption in additive manufacturing
K Venkatarao - Journal of cleaner production, 2021 - Elsevier
Quality of weld bead geometry (width and height of metal deposited) and power
consumption are still big challenges to the manufacture to control them in gas metal arc …
consumption are still big challenges to the manufacture to control them in gas metal arc …
Deep traffic congestion prediction model based on road segment grouping
Y Tu, S Lin, J Qiao, B Liu - Applied Intelligence, 2021 - Springer
Abstract Intelligent Transportation System (ITS) is now being widely built all over the world.
Traffic congestion prediction, as a major part of ITS, not only provides reliable traffic …
Traffic congestion prediction, as a major part of ITS, not only provides reliable traffic …
[HTML][HTML] Artificial intelligence enabled Digital Twins for training autonomous cars
D Chen, Z Lv - Internet of Things and Cyber-Physical Systems, 2022 - Elsevier
This exploration is aimed at the system prediction and safety performance of the Digital
Twins (DTs) of autonomous cars based on artificial intelligence technology, and the …
Twins (DTs) of autonomous cars based on artificial intelligence technology, and the …
Combination of cuckoo search and wavelet neural network for midterm building energy forecast
Z Yuan, W Wang, H Wang, S Mizzi - Energy, 2020 - Elsevier
The electrical load prediction for buildings plays a critical role in the smart-grid paradigm,
since accurate predictions provide efficient energy management. A synthetic approach has …
since accurate predictions provide efficient energy management. A synthetic approach has …
[HTML][HTML] Integrating artificial neural networks and cellular automata model for spatial-temporal load forecasting
S Zambrano-Asanza, RE Morales, JA Montalvan… - International Journal of …, 2023 - Elsevier
The long-term distribution planning should include an understanding of consumer behavior
and needs to develop strategic expansion alternatives that meet the future demand. The …
and needs to develop strategic expansion alternatives that meet the future demand. The …
An ensemble-based machine learning model for forecasting network traffic in VANET
Vehicular Ad-hoc Networks (VANETs), as the most significant element of the Intelligent
Transportation Systems (ITS), have the potential to enhance traffic efficiency and road safety …
Transportation Systems (ITS), have the potential to enhance traffic efficiency and road safety …
Spatiotemporal dynamic forecasting and analysis of regional traffic flow in urban road networks using deep learning convolutional neural network
S Wu - IEEE transactions on intelligent transportation systems, 2021 - ieeexplore.ieee.org
The purpose is to explore the spatial-temporal dynamic prediction performance of urban
road network traffic flow based on convolutional neural networks (CNN) of deep learning. A …
road network traffic flow based on convolutional neural networks (CNN) of deep learning. A …
Short-term traffic prediction using deep learning long short-term memory: taxonomy, applications, challenges, and future trends
This paper surveys the short-term road traffic forecast algorithms based on the long-short
term memory (LSTM) model of deep learning. The algorithms developed in the last three …
term memory (LSTM) model of deep learning. The algorithms developed in the last three …