[HTML][HTML] Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation
In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …
interest in reinforcement learning (RL) within the traffic and transportation community …
[HTML][HTML] The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends
J Zhang, J Wang, H Zang, N Ma, M Skitmore, Z Qu… - Sustainability, 2024 - mdpi.com
Machine learning (ML) and deep learning (DL) have become very popular in the research
community for addressing complex issues in intelligent transportation. This has resulted in …
community for addressing complex issues in intelligent transportation. This has resulted in …
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 …
A deep reinforcement learning-based cooperative approach for multi-intersection traffic signal control
Abstract Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …
Online parking assignment in an environment of partially connected vehicles: A multi-agent deep reinforcement learning approach
The advent of connected vehicles (CVs) provides new opportunities to address urban
parking issues due to the widespread application of online parking assignment (OPA) …
parking issues due to the widespread application of online parking assignment (OPA) …
Towards a sustainable monitoring: A self-powered smart transportation infrastructure skin
Sustainable monitoring of traffic using clean energy supply has always been a significant
problem for engineers. In this study, we proposed a self-powered smart transportation …
problem for engineers. In this study, we proposed a self-powered smart transportation …
[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 …
Decentralized signal control for multi-modal traffic network: A deep reinforcement learning approach
Managing traffic flow at intersections in a large-scale network remains challenging. Multi-
modal signalized intersections integrate various objectives, including minimizing the queue …
modal signalized intersections integrate various objectives, including minimizing the queue …
[HTML][HTML] Recent advances in traffic signal performance evaluation
D Leitner, P Meleby, L Miao - Journal of traffic and transportation …, 2022 - Elsevier
Signal retiming is a prominent way that transportation agencies use to fight congestion and
change of traffic pattern. Performance evaluations of traffic conditions at signalized …
change of traffic pattern. Performance evaluations of traffic conditions at signalized …
A large-scale traffic signal control algorithm based on multi-layer graph deep reinforcement learning
Due to its capability in handling complex urban intersection environments, deep
reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control …
reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control …