Two-Stage Fuzzy Traffic Congestion Detector
This 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 measurem...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Future Transportation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-7590/3/3/47 |
| _version_ | 1797579969180729344 |
|---|---|
| author | Gizem Erdinç Chiara Colombaroni Gaetano Fusco |
| author_facet | Gizem Erdinç Chiara Colombaroni Gaetano Fusco |
| author_sort | Gizem Erdinç |
| collection | DOAJ |
| description | This 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. |
| first_indexed | 2024-03-10T22:44:39Z |
| format | Article |
| id | doaj.art-b1360a8562224047a252a4d132a278ec |
| institution | Directory Open Access Journal |
| issn | 2673-7590 |
| language | English |
| last_indexed | 2024-03-10T22:44:39Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Transportation |
| spelling | doaj.art-b1360a8562224047a252a4d132a278ec2023-11-19T10:49:39ZengMDPI AGFuture Transportation2673-75902023-06-013384085710.3390/futuretransp3030047Two-Stage Fuzzy Traffic Congestion DetectorGizem Erdinç0Chiara Colombaroni1Gaetano Fusco2Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, ItalyDepartment of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, ItalyDepartment of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, ItalyThis 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.https://www.mdpi.com/2673-7590/3/3/47traffic state identificationfuzzy logiccongestion levelMamdani inference |
| spellingShingle | Gizem Erdinç Chiara Colombaroni Gaetano Fusco Two-Stage Fuzzy Traffic Congestion Detector Future Transportation traffic state identification fuzzy logic congestion level Mamdani inference |
| title | Two-Stage Fuzzy Traffic Congestion Detector |
| title_full | Two-Stage Fuzzy Traffic Congestion Detector |
| title_fullStr | Two-Stage Fuzzy Traffic Congestion Detector |
| title_full_unstemmed | Two-Stage Fuzzy Traffic Congestion Detector |
| title_short | Two-Stage Fuzzy Traffic Congestion Detector |
| title_sort | two stage fuzzy traffic congestion detector |
| topic | traffic state identification fuzzy logic congestion level Mamdani inference |
| url | https://www.mdpi.com/2673-7590/3/3/47 |
| work_keys_str_mv | AT gizemerdinc twostagefuzzytrafficcongestiondetector AT chiaracolombaroni twostagefuzzytrafficcongestiondetector AT gaetanofusco twostagefuzzytrafficcongestiondetector |