@{MTMT:35790911,
	title = {Evaluating Deep Learning Algorithms for Freeway Mainstream Traffic Control},
	url = {https://m2.mtmt.hu/api/publication/35790911},
	author = {Amini, Mehran and Karadeniz, Ahmet Mehmet and Kóczy, T. László},
	booktitle = {Proceedings of the 3rd Cognitive Mobility Conference},
	doi = {10.1007/978-3-031-81799-1_26},
	unique-id = {35790911},
	year = {2025},
	pages = {289-299},
	orcid-numbers = {Karadeniz, Ahmet Mehmet/0000-0002-6851-6033; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:35759306,
	title = {A Review Study of Fuzzy Cognitive Maps in Engineering: Applications, Insights, and Future Directions},
	url = {https://m2.mtmt.hu/api/publication/35759306},
	author = {Karatzinis, Georgios D. and Boutalis, Yiannis S.},
	doi = {10.3390/eng6020037},
	journal-iso = {ENG},
	journal = {ENG},
	volume = {6},
	unique-id = {35759306},
	abstract = {Fuzzy Cognitive Maps (FCMs) have emerged as powerful tools for addressing diverse engineering challenges, leveraging their cognitive nature and ability to encapsulate causal relationships. This paper provides a comprehensive review of FCM applications across 15 engineering sub-domains, categorizing 80 studies by their learning family, task type, and case-specific application. We analyze the methodological advancements and practical implementations of FCMs, showcasing their strengths in areas such as decision-making, classification, time-series, diagnosis, and optimization. Qualitative criteria are systematically applied to classify FCM-based methodologies, highlighting trends, practical implications of varying complexity, and human intervention across task types and learning families. However, this study also identifies key limitations, including scalability challenges, reliance on expert knowledge, and sensitivity to data distribution shifts in real-world settings. To address these issues, we outline key areas and directions for future research focusing on adaptive learning mechanisms, hybrid methodologies, and scalable computational frameworks to enhance FCM performance in dynamic and evolving contexts. The findings of this review offer a structured roadmap for advancing FCM methodologies and broadening their application scope in both contemporary and emerging engineering domains.},
	year = {2025},
	eissn = {2673-4117},
	pages = {37},
	orcid-numbers = {Karatzinis, Georgios D./0000-0003-4674-4163; Boutalis, Yiannis S./0000-0003-3717-3565}
}

@inproceedings{MTMT:35178001,
	title = {Comparative Analysis of Machine Learning Algorithms in Traffic Mainstream Control on Freeway Networks},
	url = {https://m2.mtmt.hu/api/publication/35178001},
	author = {Amini, Mehran and Kóczy, T. László},
	booktitle = {2024 IEEE 28th International Conference on Intelligent Engineering Systems (INES 2024)},
	doi = {10.1109/INES63318.2024.10629114},
	unique-id = {35178001},
	year = {2024},
	pages = {37-41},
	orcid-numbers = {Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:35431730,
	title = {Reinforcement Learning-Based Adaptive Optimal Control Model for Signal Light in Intelligent Transportation Systems},
	url = {https://m2.mtmt.hu/api/publication/35431730},
	author = {Hu, Xiaomin and He, Yuanyuan},
	doi = {10.1142/S021812662550046X},
	journal-iso = {J CIRCUIT SYST COMP},
	journal = {JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS},
	volume = {1},
	unique-id = {35431730},
	issn = {0218-1266},
	abstract = {Day-to-day mobility among the population has increased with economic growth. Smart cities are renovated with advanced technologies to admire modern life in which intelligent transportation becomes highly focused. Because the traffic signal control systems are fixed at a constant time. They split the traffic signal into predetermined intervals and function inefficiently; they result in long wait times, waste fuel and increased carbon emissions. This research study introduces a novel technique for traffic light management to reduce the uncertainties in the system. A dynamic and intelligent traffic light adaptive optimal management system (DITLAOCS) is implemented in this research. It does this by modifying the traffic signal duration in run time and using real-time traffic data as input. Furthermore, the proposed DITLAOCS executes based on three modes: fairness mode (FM), priority mode (PM) and emergent mode (EM). In fairness mode (FM), all vehicles are prioritized equally, while vehicles in different categories receive varying priority levels. Emergency vehicles, on the other hand, receive the highest priority. Furthermore, a fuzzy inference method based on traffic data is shown to choose one mode out of three (FM, PM and EM). This model uses deep reinforcement learning to switch traffic lights in three different phases (red, green and yellow). We evaluated and accurately simulated DITLAOCS on the Shaanxi city map in China using Simulation of Urban MObility (SUMO), an open-source simulator. The simulation results illustrate the efficiency of DITLAOCS when compared to other cutting-edge algorithms on several performance measures.},
	year = {2024},
	eissn = {1793-6454},
	pages = {1},
	orcid-numbers = {Hu, Xiaomin/0009-0009-5194-1584; He, Yuanyuan/0009-0008-0067-7309}
}

@inproceedings{MTMT:35571749,
	title = {Adaptive Traffic Signal Control using Genetic Algorithm for a 2×2 Traffic Network},
	url = {https://m2.mtmt.hu/api/publication/35571749},
	author = {Tan, Min Keng and Chai, Shun Quan and Ee Chuo, Helen Sin and Lim, Kit Guan and Goh, Hui Hwang and Teo, Kenneth Tze Kin},
	booktitle = {2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)},
	doi = {10.1109/IICAIET62352.2024.10730292},
	unique-id = {35571749},
	year = {2024},
	pages = {488-493}
}