@inproceedings{MTMT:34043590,
	title = {Machine learning and fuzzy cognitive maps in a hybrid approach toward freeway on-ramp traffic control},
	url = {https://m2.mtmt.hu/api/publication/34043590},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Kóczy, T. László},
	booktitle = {IEEE 17th International Symposium on Applied Computational Intelligence and Informatics SACI 2023 : Proceedings},
	doi = {10.1109/SACI58269.2023.10158585},
	unique-id = {34043590},
	year = {2023},
	pages = {000587-000592},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:32545090,
	title = {Global stability of fuzzy cognitive maps},
	url = {https://m2.mtmt.hu/api/publication/32545090},
	author = {Harmati, István Árpád and Hatwágner, F. Miklós and Kóczy, T. László},
	doi = {10.1007/s00521-021-06742-9},
	journal-iso = {NEURAL COMPUT APPL},
	journal = {NEURAL COMPUTING & APPLICATIONS},
	volume = {35},
	unique-id = {32545090},
	issn = {0941-0643},
	year = {2023},
	eissn = {1433-3058},
	pages = {7283-7295},
	orcid-numbers = {Harmati, István Árpád/0000-0002-0915-9718; Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:33272027,
	title = {A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies},
	url = {https://m2.mtmt.hu/api/publication/33272027},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Kóczy, T. László},
	doi = {10.3390/math10214139},
	journal-iso = {MATHEMATICS-BASEL},
	journal = {MATHEMATICS},
	volume = {10},
	unique-id = {33272027},
	abstract = {Freeway networks, despite being built to handle the transportation needs of large traffic volumes, have suffered in recent years from an increase in demand that is rarely resolvable through infrastructure improvements. Therefore, the implementation of particular control methods constitutes, in many instances, the only viable solution for enhancing the performance of freeway traffic systems. The topic is fraught with ambiguity, and there is no tool for understanding the entire system mathematically; hence, a fuzzy suggested algorithm seems not just appropriate but essential. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system are proposed as tools to analyze freeway traffic data with the objective of traffic flow modeling at a macroscopic level in order to address congestion-related issues as the primary goal of the traffic control strategies. In addition to presenting a framework of fuzzy system-based controllers in freeway traffic, the results of this study demonstrated that a fuzzy inference system and fuzzy cognitive maps are capable of congestion level prediction, traffic flow simulation, and scenario analysis, thereby enhancing the performance of the traffic control strategies involving the implementation of ramp management policies, controlling vehicle movement within the freeway by mainstream control, and routing control.},
	year = {2022},
	eissn = {2227-7390},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@inproceedings{MTMT:33049054,
	title = {Fuzzy System-Based Solutions for Traffic Control in Freeway Networks Toward Sustainable Improvement},
	url = {https://m2.mtmt.hu/api/publication/33049054},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Kóczy, T. László},
	booktitle = {Information Processing and Management of Uncertainty in Knowledge-Based Systems: 19th International Conference, IPMU 2022 : Proceedings, Part II},
	doi = {10.1007/978-3-031-08974-9_23},
	volume = {1602 CCIS},
	unique-id = {33049054},
	year = {2022},
	pages = {288-305},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@inproceedings{MTMT:32678952,
	title = {Novel Methods of FCM Model Reduction},
	url = {https://m2.mtmt.hu/api/publication/32678952},
	author = {Hatwágner, F. Miklós and Kóczy, T. László},
	booktitle = {Computational Intelligence and Mathematics for Tackling Complex Problems 2},
	doi = {10.1007/978-3-030-88817-6_12},
	unique-id = {32678952},
	year = {2022},
	pages = {101-112},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:32556112,
	title = {A vehicular traffic congestion predictor system using Mamdani fuzzy inference},
	url = {https://m2.mtmt.hu/api/publication/32556112},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Mikulai, Gergely Csaba and Kóczy, T. László},
	doi = {10.52846/stccj.2021.1.2.27},
	journal-iso = {SYST THEORY CONTROL COMP J},
	journal = {SYSTEM THEORY CONTROL AND COMPUTING JOURNAL},
	volume = {1},
	unique-id = {32556112},
	issn = {2668-2966},
	year = {2021},
	eissn = {2810-4099},
	pages = {49-57},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:32537879,
	title = {Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation},
	url = {https://m2.mtmt.hu/api/publication/32537879},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Mikulai, Gergely Csaba and Kóczy, T. László},
	doi = {10.36244/ICJ.2021.3.2},
	journal-iso = {INFOCOMM J},
	journal = {INFOCOMMUNICATIONS JOURNAL},
	volume = {13},
	unique-id = {32537879},
	issn = {2061-2079},
	abstract = {Fuzzy cognitive maps (FCM) have been broadly employed to analyze complex and decidedly uncertain systems in modeling, forecasting, decision making, etc. Road traffic flow is also notoriously known as a highly uncertain nonlinear and complex system. Even though applications of FCM in risk analysis have been presented in various engineering fields, this research aims at modeling road traffic flow based on macroscopic characteristics through FCM. Therefore, a simulation of variables involved with road traffic flow carried out through FCM reasoning on historical data collected from the e-toll dataset of Hungarian networks of freeways. The proposed FCM model is developed based on 58 selected freeway segments as the “concepts” of the FCM; moreover, a new inference rule for employing in FCM reasoning process along with its algorithms have been presented. The results illustrate FCM representation and computation of the real segments with their main road traffic-related characteristics that have reached an equilibrium point. Furthermore, a simulation of the road traffic flow by performing the analysis of customized scenarios is presented, through which macroscopic modeling objectives such as predicting future road traffic flow state, route guidance in various scenarios, freeway geometric characteristics indication, and effectual mobility can be evaluated.},
	year = {2021},
	eissn = {2061-2125},
	pages = {14-23},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@inproceedings{MTMT:32101466,
	title = {An intelligent traffic congestion detection approach based on fuzzy inference system},
	url = {https://m2.mtmt.hu/api/publication/32101466},
	author = {Amini, Mehran and Hatwágner, F. Miklós and Mikulai, Gergely Csaba and Kóczy, T. László},
	booktitle = {15th IEEE International Symposium on Applied Computational Intelligence and Informatics SACI 2021},
	doi = {10.1109/SACI51354.2021.9465637},
	unique-id = {32101466},
	abstract = {Traffic congestion causes significant economic and social consequences. Instant detection of vehicular traffic breakdown has a pivotal role in intelligent transportation engineering. Common traffic estimators and predictors systems need traffic observations to be classified in their binary-set-nature computation methods which are unable to be an effective base for traffic modeling, since they are defined by precise and deterministic characteristics while traffic is known to be a highly complex and nonlinear system, which may be prescribed by uncertain models containing vague properties. This study aims at applying a new fuzzy inference model for predicting the level of congestion in such heterogeneous and convoluted networks, where the paucity of accurate and real-time data can cause problems in interpreting the whole system state by conventional quantitative techniques. The proposed fuzzy inference model is based on real data extracted from Hungarian network of freeways. As input variables traffic flow and approximate capacity of each segment are considered and level of congestion is regarded as output variable. In the model, a total number of 75 rules were developed on the basis of available datasets, percentile distribution, and experts' judgments. Designed model and analyzing steps are simulated and proven by Matlab fuzzy logic toolbox. The results illustrate correlations and relationships among input variables with predicting the level of congestion based on available resources. Furthermore, performed analyses beside their tractability in dealing with ambiguity and subjectivity are aligned with intelligent traffic modeling purposes in designing traffic breakdown-related alert or early warning systems, infrastructure and services planning, and sustainability development.},
	year = {2021},
	pages = {97-104},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:31809177,
	title = {Stability of Fixed-Point Values in Reduced Fuzzy Cognitive Map Models},
	url = {https://m2.mtmt.hu/api/publication/31809177},
	author = {Hatwágner, F. Miklós and Kóczy, T. László},
	doi = {10.1007/978-3-030-47124-8_29},
	journal-iso = {STUD FUZZ SOFT COMP},
	journal = {STUDIES IN FUZZINESS AND SOFT COMPUTING},
	volume = {393},
	unique-id = {31809177},
	issn = {1434-9922},
	year = {2021},
	pages = {359-372},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}

@inbook{MTMT:31430826,
	title = {A Novel Approach to Analyze the Behavior of Fuzzy Cognitive Maps},
	url = {https://m2.mtmt.hu/api/publication/31430826},
	author = {Hatwágner, F. Miklós and Kóczy, T. László},
	booktitle = {Kutatási jelentés - Research Report, 2. kötet},
	unique-id = {31430826},
	year = {2020},
	pages = {351-356},
	orcid-numbers = {Hatwágner, F. Miklós/0000-0002-5545-2018; Kóczy, T. László/0000-0003-1316-4832}
}