Fuzzy Inference System for Congestion Index Estimation Based on Speed Probability Distributions
| Autor: | Leo Tišljarić, Edouard Ivanjko, Tonči Carić, Zvonko Kavran |
|---|---|
| Přispěvatelé: | Bujňák, Jan, Guagliano, Mario |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
traffic congestion index speed frequency distribution speed transition matrix GNSS data center of mass
Highway Capacity Manual Index (economics) Traffic congestion Computer science Genetic algorithm Probability distribution Domain knowledge Satellite system Data mining computer.software_genre computer Intelligent transportation system |
| DOI: | 10.1016/j.trpro.2021.07.124 |
| Popis: | The increased development of the urban areas consequently results in a larger number of vehicles on the road network, which inevitably leads to traffic congestion, especially in the rush hours. Intelligent transport systems solutions present the applications that can be useful in detecting and dealing with the problems that are related to congestion. This paper presents a method for the congestion index estimation using the speed transition matrix and the corresponding center of mass. The congestion index is estimated using a Fuzzy Inference System optimized by adopting the Genetic Algorithm. In this paper, the large real-world Global Navigation Satellite System data are used to evaluate the proposed method for the traffic state estimation of most relevant road segments in the largest city in Croatia, the City of Zagreb. The validation of results is performed using the domain knowledge presented in the Highway Capacity Manual, which resulted in the model’s precision of 94.6%. The result indicates a possible application of the method for the congestion estimation in urban centers. |
| Databáze: | OpenAIRE |
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