Survey on artificial intelligence (AI) techniques for vehicular ad-hoc networks (VANETs)
A Mchergui, T Moulahi, S Zeadally - Vehicular Communications, 2022 - Elsevier
Advances in communications, smart transportation systems, and computer systems have
recently opened up vast possibilities of intelligent solutions for traffic safety, convenience …
recently opened up vast possibilities of intelligent solutions for traffic safety, convenience …
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 …
Ising-traffic: Using ising machine learning to predict traffic congestion under uncertainty
This paper addresses the challenges in accurate and real-time traffic congestion prediction
under uncertainty by proposing Ising-Traffic, a dual-model Ising-based traffic prediction …
under uncertainty by proposing Ising-Traffic, a dual-model Ising-based traffic prediction …
[HTML][HTML] Applications of deep learning in congestion detection, prediction and alleviation: A survey
Detecting, predicting, and alleviating traffic congestion are targeted at improving the level of
service of the transportation network. With increasing access to larger datasets of higher …
service of the transportation network. With increasing access to larger datasets of higher …
Spatial–temporal complex graph convolution network for traffic flow prediction
Traffic flow prediction remains an ongoing hot topic in the field of Intelligent Transportation
System. The state-of-the-art traffic flow prediction models can effectively extract both spatial …
System. The state-of-the-art traffic flow prediction models can effectively extract both spatial …
A Long Short-Term Memory-based correlated traffic data prediction framework
Correlated traffic data refers to a collection of time series recorded simultaneously in
different regions throughout the same transportation network route. Due to the presence of …
different regions throughout the same transportation network route. Due to the presence of …
A new hybrid deep learning algorithm for prediction of wide traffic congestion in smart cities
The vehicular adhoc network (VANET) is an emerging research topic in the intelligent
transportation system that furnishes essential information to the vehicles in the network …
transportation system that furnishes essential information to the vehicles in the network …
Traffic flow forecasting in the covid-19: A deep spatial-temporal model based on discrete wavelet transformation
Traffic flow prediction has always been the focus of research in the field of Intelligent
Transportation Systems, which is conducive to the more reasonable allocation of basic …
Transportation Systems, which is conducive to the more reasonable allocation of basic …
Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning
The development of the Internet of Things (IoT) has produced new innovative solutions, such
as smart cities, which enable humans to have a more efficient, convenient and smarter way …
as smart cities, which enable humans to have a more efficient, convenient and smarter way …
Deep learning for weather forecasting: A cnn-lstm hybrid model for predicting historical temperature data
Y Gong, Y Zhang, F Wang, CH Lee - arXiv preprint arXiv:2410.14963, 2024 - arxiv.org
As global climate change intensifies, accurate weather forecasting has become increasingly
important, affecting agriculture, energy management, environmental protection, and daily …
important, affecting agriculture, energy management, environmental protection, and daily …