TY - CHAP AU - Amini, Mehran AU - Hatwágner, F. Miklós AU - Kóczy, T. László ED - Szakál, Anikó TI - Machine learning and fuzzy cognitive maps in a hybrid approach toward freeway on-ramp traffic control T2 - IEEE 17th International Symposium on Applied Computational Intelligence and Informatics SACI 2023 : Proceedings PB - IEEE Hungary Section CY - Budapest SN - 9798350321104 PY - 2023 SP - 000587 EP - 000592 PG - 6 DO - 10.1109/SACI58269.2023.10158585 UR - https://m2.mtmt.hu/api/publication/34043590 ID - 34043590 N1 - Export Date: 31 July 2023 Correspondence Address: Amini, M.; Szechenyi Istvan University, Hungary; email: mehran@sze.hu LA - English DB - MTMT ER - TY - JOUR AU - Harmati, István Árpád AU - Hatwágner, F. Miklós AU - Kóczy, T. László TI - Global stability of fuzzy cognitive maps JF - NEURAL COMPUTING & APPLICATIONS J2 - NEURAL COMPUT APPL VL - 35 PY - 2023 IS - 10 SP - 7283 EP - 7295 PG - 13 SN - 0941-0643 DO - 10.1007/s00521-021-06742-9 UR - https://m2.mtmt.hu/api/publication/32545090 ID - 32545090 LA - English DB - MTMT ER - TY - JOUR AU - Amini, Mehran AU - Hatwágner, F. Miklós AU - Kóczy, T. László TI - A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies JF - MATHEMATICS J2 - MATHEMATICS-BASEL VL - 10 PY - 2022 IS - 21 PG - 17 SN - 2227-7390 DO - 10.3390/math10214139 UR - https://m2.mtmt.hu/api/publication/33272027 ID - 33272027 N1 - Department of Informatics, Szechenyi Istvan University, Gyor, 9026, Hungary Department of Telecommunication and Media Informatics, Budapest University of Technology and Economics, Budapest, 1111, Hungary Cited By :1 Export Date: 3 April 2023 Correspondence Address: Amini, M.; Department of Informatics, Hungary; email: mehran@sze.hu Correspondence Address: Koczy, L.T.; Department of Informatics, Hungary; email: koczy@tmit.bme.hu AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Amini, Mehran AU - Hatwágner, F. Miklós AU - Kóczy, T. László ED - Ciucci, Davide ED - Couso, Inés ED - Medina, Jesús ED - Ślęzak, Dominik ED - Petturiti, Davide ED - Bouchon-Meunier, Bernadette ED - Yager, Ronald R TI - Fuzzy System-Based Solutions for Traffic Control in Freeway Networks Toward Sustainable Improvement T2 - Information Processing and Management of Uncertainty in Knowledge-Based Systems: 19th International Conference, IPMU 2022 : Proceedings, Part II VL - 1602 CCIS PB - Springer International Publishing CY - Cham SN - 9783031089749 T3 - Communications in Computer and Information Science, ISSN 1865-0929 ; 1602. PY - 2022 SP - 288 EP - 305 PG - 18 DO - 10.1007/978-3-031-08974-9_23 UR - https://m2.mtmt.hu/api/publication/33049054 ID - 33049054 N1 - Department of Information Technology, Szechenyi Istvan University, Gyor, Hungary Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary Export Date: 12 August 2022 Correspondence Address: Amini, M.; Department of Information Technology, Hungary; email: mehran@sze.hu LA - English DB - MTMT ER - TY - CHAP AU - Hatwágner, F. Miklós AU - Kóczy, T. László ED - Moreno-García, Juan ED - Medina-Moreno, Jesús ED - Kóczy, T. László ED - Cornejo, María Eugenia TI - Novel Methods of FCM Model Reduction T2 - Computational Intelligence and Mathematics for Tackling Complex Problems 2 PB - Springer Cham CY - Cham SN - 9783030888169 T3 - Studies in Computational Intelligence, ISSN 1860-949X ; 955. PY - 2022 SP - 101 EP - 112 PG - 12 DO - 10.1007/978-3-030-88817-6_12 UR - https://m2.mtmt.hu/api/publication/32678952 ID - 32678952 N1 - Conference code: 271109 Export Date: 7 April 2022 Correspondence Address: Hatwágner, M.F.; Department of Information Technology, Hungary; email: miklos.hatwagner@sze.hu Funding details: Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFI, K108405, K124055 Funding text 1: The paper was written with the support of the project titled “Internationalisation, initiatives to establish a new source of researchers and graduates and development of knowledge and technological transfer as instruments of intelligent specialisations at Széchenyi István University” (project number: EFOP-3.6.1-16-2016-00017). M. F. H. acknowledges the financial support of the DE Excellence Program. L. T. K. is supported by NKFIH K108405 and K124055 grants. LA - English DB - MTMT ER - TY - JOUR AU - Amini, Mehran AU - Hatwágner, F. Miklós AU - Mikulai, Gergely Csaba AU - Kóczy, T. László TI - A vehicular traffic congestion predictor system using Mamdani fuzzy inference JF - SYSTEM THEORY CONTROL AND COMPUTING JOURNAL J2 - SYST THEORY CONTROL COMP J VL - 1 PY - 2021 IS - 2 SP - 49 EP - 57 PG - 9 SN - 2668-2966 DO - 10.52846/stccj.2021.1.2.27 UR - https://m2.mtmt.hu/api/publication/32556112 ID - 32556112 LA - English DB - MTMT ER - TY - JOUR AU - Amini, Mehran AU - Hatwágner, F. Miklós AU - Mikulai, Gergely Csaba AU - Kóczy, T. László TI - Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation JF - INFOCOMMUNICATIONS JOURNAL J2 - INFOCOMM J VL - 13 PY - 2021 IS - 3 SP - 14 EP - 23 PG - 10 SN - 2061-2079 DO - 10.36244/ICJ.2021.3.2 UR - https://m2.mtmt.hu/api/publication/32537879 ID - 32537879 N1 - Department of Information Technology, Szechenyi Istvan University, Gyor, Hungary Doctoral School of Regional Sciences and Business Administration, Gyor, Hungary Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Hungary Export Date: 18 January 2022 Correspondence Address: Amini, M.; Department of Information Technology, Hungary Funding details: National Research, Development and Innovation Office, NKFIH K124055 Funding text 1: This research was supported by the National Office of Research, Development, and Innovation grant NKFIH K124055. AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Amini, Mehran AU - Hatwágner, F. Miklós AU - Mikulai, Gergely Csaba AU - Kóczy, T. László ED - Szakál, Anikó TI - An intelligent traffic congestion detection approach based on fuzzy inference system T2 - 15th IEEE International Symposium on Applied Computational Intelligence and Informatics SACI 2021 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Budapest CY - Piscataway (NJ) SN - 9781728195438 PY - 2021 SP - 97 EP - 104 PG - 8 DO - 10.1109/SACI51354.2021.9465637 UR - https://m2.mtmt.hu/api/publication/32101466 ID - 32101466 N1 - Funding Agency and Grant Number: National Research, Development and Innovation Office NKFIHNational Research, Development & Innovation Office (NRDIO) - Hungary [K124055] Funding text: This research was supported by the National Research, Development and Innovation Office NKFIH grant Nr. K124055. AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Hatwágner, F. Miklós AU - Kóczy, T. László TI - Stability of Fixed-Point Values in Reduced Fuzzy Cognitive Map Models JF - STUDIES IN FUZZINESS AND SOFT COMPUTING J2 - STUD FUZZ SOFT COMP VL - 393 PY - 2021 SP - 359 EP - 372 PG - 14 SN - 1434-9922 DO - 10.1007/978-3-030-47124-8_29 UR - https://m2.mtmt.hu/api/publication/31809177 ID - 31809177 LA - English DB - MTMT ER - TY - CHAP AU - Hatwágner, F. Miklós AU - Kóczy, T. László ED - Dernóczy, Adrienn TI - A Novel Approach to Analyze the Behavior of Fuzzy Cognitive Maps T2 - Kutatási jelentés - Research Report, 2. kötet PB - Universitas-Győr Nonprofit Kft. CY - Győr T3 - Kutatási jelentés (Széchenyi István Egyetem, Győr), ISSN 2676-7937 ; 2. PY - 2020 SP - 351 EP - 356 PG - 6 UR - https://m2.mtmt.hu/api/publication/31430826 ID - 31430826 LA - English DB - MTMT ER -