[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 …
Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G
Employing machine learning into 6G vehicular networks to support vehicular application
services is being widely studied and a hot topic for the latest research works in the literature …
services is being widely studied and a hot topic for the latest research works in the literature …
A survey on intelligent control for multiagent systems
In practice, the dual constraints of limited interaction capabilities and system uncertainties
make it difficult for large-scale multiagent systems (MASs) to achieve intelligent collaboration …
make it difficult for large-scale multiagent systems (MASs) to achieve intelligent collaboration …
Solving dynamic traveling salesman problems with deep reinforcement learning
A traveling salesman problem (TSP) is a well-known NP-complete problem. Traditional TSP
presumes that the locations of customers and the traveling time among customers are fixed …
presumes that the locations of customers and the traveling time among customers are fixed …
Intelligent traffic management system based on the internet of vehicles (IoV)
SA Elsagheer Mohamed… - Journal of advanced …, 2021 - Wiley Online Library
The present era is marked by rapid improvement and advances in technology. One of the
most essential areas that demand improvement is the traffic signal, as it constitutes the core …
most essential areas that demand improvement is the traffic signal, as it constitutes the core …
Intelligent vehicle pedestrian light (IVPL): A deep reinforcement learning approach for traffic signal control
Deep reinforcement learning (RL) has been widely studied in traffic signal control. Despite
the promising results that indicate the superiority of deep RL in terms of the quality of …
the promising results that indicate the superiority of deep RL in terms of the quality of …
Deep reinforcement learning for smart grid operations: algorithms, applications, and prospects
With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …
more complicated power system with high uncertainty is gradually formed, which brings …
An intelligent IoT based traffic light management system: deep reinforcement learning
Traffic is one of the indispensable problems of modern societies, which leads to undesirable
consequences such as time wasting and greater possibility of accidents. Adaptive Traffic …
consequences such as time wasting and greater possibility of accidents. Adaptive Traffic …
Multi-agent broad reinforcement learning for intelligent traffic light control
Intelligent traffic light control (ITLC) aims to relieve traffic congestion. Some multi-agent deep
reinforcement learning (MADRL) algorithms have been proposed for ITLC, and most of them …
reinforcement learning (MADRL) algorithms have been proposed for ITLC, and most of them …
A comprehensive survey of the key technologies and challenges surrounding vehicular ad hoc networks
Vehicular ad hoc networks (VANETs) and the services they support are an essential part of
intelligent transportation. Through physical technologies, applications, protocols, and …
intelligent transportation. Through physical technologies, applications, protocols, and …