The paper presents a two-stage fuzzy-logic application based on the Mamdani inference method to classify the observed road traffic conditions. It was tested using real data extracted from the Padua–Venice motorway in Italy, which contains a dense monitoring network that provides continuous measurements of flow, occupancy, and speed. The data collected indicate that the traffic flow characteristics of the road network are highly perturbed in oversaturated conditions, suggesting that a fuzzy approach might be more convenient than a deterministic one. Furthermore, since drivers have a vague notion of the traffic state, the fuzzy method seems more appropriate than the deterministic one for providing drivers with qualitative information about current traffic conditions. In the proposed method, the traffic states are analysed for each road section by relating them to average speed values modelled with fuzzy rules. An application using real data was carried out in Simulink MATLAB. The empirical results show that the proposed study performs well in estimation and classification.

Two-Stage Fuzzy Traffic Congestion Detector / Erdinç, Gizem; Colombaroni, Chiara; Fusco, Gaetano. - In: FUTURE TRANSPORTATION. - ISSN 2673-7590. - 3:3(2023), pp. 840-857. [10.3390/futuretransp3030047]

Two-Stage Fuzzy Traffic Congestion Detector

Gizem Erdinç
Software
;
Chiara Colombaroni
Methodology
;
Gaetano Fusco
Supervision
2023

Abstract

The paper presents a two-stage fuzzy-logic application based on the Mamdani inference method to classify the observed road traffic conditions. It was tested using real data extracted from the Padua–Venice motorway in Italy, which contains a dense monitoring network that provides continuous measurements of flow, occupancy, and speed. The data collected indicate that the traffic flow characteristics of the road network are highly perturbed in oversaturated conditions, suggesting that a fuzzy approach might be more convenient than a deterministic one. Furthermore, since drivers have a vague notion of the traffic state, the fuzzy method seems more appropriate than the deterministic one for providing drivers with qualitative information about current traffic conditions. In the proposed method, the traffic states are analysed for each road section by relating them to average speed values modelled with fuzzy rules. An application using real data was carried out in Simulink MATLAB. The empirical results show that the proposed study performs well in estimation and classification.
2023
traffic state identification; fuzzy logic; congestion level; Mamdani inference
01 Pubblicazione su rivista::01a Articolo in rivista
Two-Stage Fuzzy Traffic Congestion Detector / Erdinç, Gizem; Colombaroni, Chiara; Fusco, Gaetano. - In: FUTURE TRANSPORTATION. - ISSN 2673-7590. - 3:3(2023), pp. 840-857. [10.3390/futuretransp3030047]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1687990
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