A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/21/4139 |
| _version_ | 1827646225801805824 |
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| author | Mehran Amini Miklos F. Hatwagner Laszlo T. Koczy |
| author_facet | Mehran Amini Miklos F. Hatwagner Laszlo T. Koczy |
| author_sort | Mehran Amini |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T18:52:01Z |
| format | Article |
| id | doaj.art-882d495b09aa42b09f4fa2bad04e6850 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T18:52:01Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-882d495b09aa42b09f4fa2bad04e68502023-11-24T05:45:30ZengMDPI AGMathematics2227-73902022-11-011021413910.3390/math10214139A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control StrategiesMehran Amini0Miklos F. Hatwagner1Laszlo T. Koczy2Department of Informatics, Szechenyi Istvan University, 9026 Gyor, HungaryDepartment of Informatics, Szechenyi Istvan University, 9026 Gyor, HungaryDepartment of Informatics, Szechenyi Istvan University, 9026 Gyor, HungaryFreeway 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.https://www.mdpi.com/2227-7390/10/21/4139fuzzy systeminference systemfuzzy cognitive mapcongestion predictioncontrol strategyfreeway networks |
| spellingShingle | Mehran Amini Miklos F. Hatwagner Laszlo T. Koczy A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies Mathematics fuzzy system inference system fuzzy cognitive map congestion prediction control strategy freeway networks |
| title | A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies |
| title_full | A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies |
| title_fullStr | A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies |
| title_full_unstemmed | A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies |
| title_short | A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies |
| title_sort | combined approach of fuzzy cognitive maps and fuzzy rule based inference supporting freeway traffic control strategies |
| topic | fuzzy system inference system fuzzy cognitive map congestion prediction control strategy freeway networks |
| url | https://www.mdpi.com/2227-7390/10/21/4139 |
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