[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 learning support for intelligent transportation systems
J Guerrero‐Ibañez… - Transactions on …, 2021 - Wiley Online Library
Abstract Intelligent Transportation Systems (ITS) help improve the ever‐increasing vehicular
flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation …
flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation …
Deep reinforcement learning for intelligent transportation systems: A survey
Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …
approaches bring out a new research direction for all control-based systems, eg, in …
Deep learning for intelligent transportation systems: A survey of emerging trends
M Veres, M Moussa - IEEE Transactions on Intelligent …, 2019 - ieeexplore.ieee.org
Transportation systems operate in a domain that is anything but simple. Many exhibit both
spatial and temporal characteristics, at varying scales, under varying conditions brought on …
spatial and temporal characteristics, at varying scales, under varying conditions brought on …
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 …
Cooperative signal-free intersection control using virtual platooning and traffic flow regulation
The emerging technologies of connectivity and automation enable the potential for signal-
free intersection control. In this context, virtual platooning is posited to be an innovative …
free intersection control. In this context, virtual platooning is posited to be an innovative …
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 …
Traffic signal optimization for partially observable traffic system and low penetration rate of connected vehicles
Observability and controllability are two critical requirements for a partially observable
transportation system. This paper proposes a stepwise signal optimization framework with …
transportation system. This paper proposes a stepwise signal optimization framework with …
[HTML][HTML] CCGN: Centralized collaborative graphical transformer multi-agent reinforcement learning for multi-intersection signal free-corridor
Tackling traffic signal control through multi-agent reinforcement learning is a widely-
employed approach. However, current state-of-the-art models have drawbacks: intersections …
employed approach. However, current state-of-the-art models have drawbacks: intersections …
A novel deep deterministic policy gradient model applied to intelligent transportation system security problems in 5G and 6G network scenarios
Traffic congestion has been an actual problem in large cities, causing personal
inconvenience and environmental pollution. To solve this problem, new applications for …
inconvenience and environmental pollution. To solve this problem, new applications for …