[HTML][HTML] Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation

Y Han, M Wang, L Leclercq - Communications in Transportation Research, 2023 - Elsevier
In recent years, the advancement of artificial intelligence techniques has led to significant
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 …

Intelligent vehicle pedestrian light (IVPL): A deep reinforcement learning approach for traffic signal control

M Yazdani, M Sarvi, SA Bagloee, N Nassir… - … research part C …, 2023 - Elsevier
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 …

A deep reinforcement learning-based cooperative approach for multi-intersection traffic signal control

TA Haddad, D Hedjazi, S Aouag - Engineering Applications of Artificial …, 2022 - Elsevier
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) …

Online parking assignment in an environment of partially connected vehicles: A multi-agent deep reinforcement learning approach

X Zhang, C Zhao, F Liao, X Li, Y Du - Transportation Research Part C …, 2022 - Elsevier
The advent of connected vehicles (CVs) provides new opportunities to address urban
parking issues due to the widespread application of online parking assignment (OPA) …

Towards a sustainable monitoring: A self-powered smart transportation infrastructure skin

Q Zheng, Y Hou, H Yang, P Tan, H Shi, Z Xu, Z Ye… - Nano Energy, 2022 - Elsevier
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 …

[HTML][HTML] CCGN: Centralized collaborative graphical transformer multi-agent reinforcement learning for multi-intersection signal free-corridor

H Mukhtar, A Afzal, S Alahmari, S Yonbawi - Neural Networks, 2023 - Elsevier
Tackling traffic signal control through multi-agent reinforcement learning is a widely-
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

J Yu, PA Laharotte, Y Han, L Leclercq - Transportation Research Part C …, 2023 - Elsevier
Managing traffic flow at intersections in a large-scale network remains challenging. Multi-
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 …

A large-scale traffic signal control algorithm based on multi-layer graph deep reinforcement learning

T Wang, Z Zhu, J Zhang, J Tian, W Zhang - Transportation Research Part C …, 2024 - Elsevier
Due to its capability in handling complex urban intersection environments, deep
reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control …