Phd in INFRASTRUCTURES AND TRANSPORT

Thesis title: Two-stage fuzzy traffic state identifier

This thesis presents the two-stage fuzzy logic application based on the Mamdani inference method for classifying observed road traffic conditions. It was tested with 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 collected data indicates that the traffic flow characteristics of the road network are highly perturbed under oversaturated conditions, suggesting that a fuzzy approach may 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 method for providing drivers with qualitative information about current traffic conditions. In the proposed method, the traffic states were analyzed for each road section by relating them to the average congestion levels and average speed values predicted using fuzzy rules. An application using real data was developed using MATLAB Simulink. The empirical results demonstrated that the proposed method performs well in terms of estimation and classification.