Vehicule identification from inductive loops application : Travel time estimation for a mixed population of cars and trucks

This paper addresses the use of existing widespread Inductive Loops Detector (ILD) Network for realizing an estimation of individual travel time for a mixed population of cars and trucks. The aim is to provide traffic information to both users and traffic managers. The identification of vehicles is realized by comparing the destination inductive signature features with the origin inductive signature features using an identification method. In this paper, we propose to use three identification methods : a Bayesian based learning approach, a fuzzy logic method and the SVM method. These methods are evaluated on a real site. In order to increase the level of identification, several propositions are carried out and discussed.