A vehicular traffic congestion predictor system using Mamdani fuzzy inference

Mehran Amini; Miklos F. Hatwagner; Gergely Cs.  Mikulai; Laszlo T. Koczy

doi:10.52846/stccj.2021.1.2.27

Authors

Mehran Amini Department of Information Technology, Szechenyi Istvan University Gyor, Hungary
Miklos F. Hatwagner Department of Information Technology Szechenyi Istvan University Gyor, Hungary
Gergely Cs. Mikulai Doctoral School of Regional Sciences and Business Administration Budapest, Hungary
Laszlo T. Koczy Department of Information Technology Szechenyi Istvan University Gyor, Hungary Department of Telecommunications and Media Informatics Budapest, Hungary

DOI:

https://doi.org/10.52846/stccj.2021.1.2.27

Keywords:

Fuzzy inference, intelligent transportation, congestion prediction

Abstract

The process of traffic control systems significantly relies on the immediate detection of breakdown states. As a result of their crisp (non-fuzzy) based calculation procedures, conventional traffic estimators and predictors cannot effectively model traffic states. In fact, these methods are characterized by exact features, while traffic is defined by uncertain variables with vague properties. Furthermore, typical numerical methodologies have constraints on evaluating the overall system status in heterogeneous and convoluted networks mainly due to the absence of reliable and real-time data. This study develops a fuzzy inference system that uses data from the Hungarian freeway networks for predicting the severity of congestion in this complex network. Congestion severity is considered the output variable, and traffic flow along with the length and the number of lanes of each section are assigned as input variables. Seventy-five fuzzy production rules were generated using accessible datasets, percentile distribution, and experts' consensus. The MATLAB fuzzy logic toolbox simulates the designed model and analysis steps. According to available resources, the results demonstrate linkages among input variables. Analyses are also used to construct intelligent traffic modeling systems and further service-related planning.

Author Biographies

Mehran Amini, Department of Information Technology, Szechenyi Istvan University Gyor, Hungary

Mehran Amini is a Ph.D. candidate in computer science in the Department of Information Technology at Széchenyi István University, Győr, Hungary. He has almost a decade of professional data analysis expertise, primarily in business intelligence. Computational intelligence and machine learning algorithms in modeling complex systems and risk analysis are among his main research interests. He also teaches Bioinformatics and IT project management.
Miklos F. Hatwagner, Department of Information Technology Szechenyi Istvan University Gyor, Hungary

Miklos F. Hatwagner is an Associate Professor in the Department of Information Technology at Széchenyi István University, Győr, Hungary. He holds a Ph.D. in Information Science from Széchenyi István University (September 2013). He has been working for over ten years as a Researcher in several research projects related to the development of novel parallel implementations of various evolutionary algorithms, the effective error handling techniques in distributed environments, and their application. He has been involved in several national research projects. He was also a member of the Hungarian ENUM project team. Later he turned his attention to the Fuzzy Cognitive Maps (FCM), the training and application of them to solve several problems arose in the fields of management, environmental protection, etc. He is the author or co-author of approx. 50 conference or journal papers. He has over 100 citations from independent researchers (h-index = 8 in Google Scholar and hindex = 7 in Scopus). His research interests include evolutionary algorithms, optimization, parallel computing, info-communication, Fuzzy Cognitive Maps, decision support, machine learning.
Gergely Cs. Mikulai, Doctoral School of Regional Sciences and Business Administration Budapest, Hungary

Gergely Cs. Mikulai received the B.Sc. degree in Mechanical Engineering at Budapest University of Technology and Economics (BME) in 2016. He received an M.Sc. degree in Business Development at Óbuda University in 2018. He is currently with Ph.D. Programme of Regional and Economic Sciences with Transdisciplinarity focus. His research interests mainly include route selection issues, using mostly fuzzy signature rule base evaluation.
Laszlo T. Koczy, Department of Information Technology Szechenyi Istvan University Gyor, Hungary Department of Telecommunications and Media Informatics Budapest, Hungary

Laszlo T. Koczy received the M.Sc., M.Phil. and Ph.D. degrees from the Technical University of Budapest (BME) in 1975, 1976, and 1977, respectively; and the D.Sc. degree from the Hungarian Academy of Science in 1998. He spent his career at BME until 2001 and from 2002 at Szechenyi Istvan University (Gyor, SZE). He has been a visiting professor in Australia, Japan, Korea, Austria, Italy, etc. His research interests are fuzzy systems, evolutionary and memetic algorithms, and neural networks, as well as applications in infocommunications, logistics, management, and others. In the last years, he has focused on NP-complete problems, especially route selection and optimization and the application of meta-heuristics for approximate solution of such complex tasks. He has published over 775 articles, most of those being refereed papers, and several textbooks on the subject. His Hirsch-index is 40 by Google Scholar (based on 7300 citations there).

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