DocumentCode :
2365456
Title :
Vehicule identification from inductive loops application : Travel time estimation for a mixed population of cars and trucks
Author :
Bastard, Cédric Le ; Guilbert, David ; Delepoulle, Antoine ; Boubezoul, Abderrahmane ; Ieng, Sio-Song ; Wang, Yide
Author_Institution :
CETE de l´´Ouest, France
fYear :
2011
fDate :
5-7 Oct. 2011
Firstpage :
507
Lastpage :
512
Abstract :
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.
Keywords :
Bayes methods; automobiles; estimation theory; fuzzy logic; identification; learning (artificial intelligence); sensors; support vector machines; Bayesian based learning approach; SVM method; fuzzy logic method; individual travel time estimation; inductive loops detector network; inductive signature feature; traffic manager; travel time estimation; vehicle identification; Bayesian methods; Databases; Electromagnetics; Kernel; Support vector machines; Vectors; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on
Conference_Location :
Washington, DC
ISSN :
2153-0009
Print_ISBN :
978-1-4577-2198-4
Type :
conf
DOI :
10.1109/ITSC.2011.6082802
Filename :
6082802
Link To Document :
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