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Proceedings Paper

Classifiers and distance-based evidential fusion for road travel time estimation
Author(s): N.-E. El Faouzi; E. Lefevre
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Paper Abstract

This paper addresses the road travel time estimation on an urban axis by classification method based on evidence theory. The travel time (TT) indicator can be used either for traffic management or for drivers' information. The information used to estimate the travel time (induction loop sensor, cameras, probe vehicle,...) is complementary and redundant. It is then necessary to implement strategies of multi-sensors data fusion. The selected framework is the evidence theory. This theory takes more into account the imprecision and uncertainty of multisource information. Two strategies were implemented. The first one is classifier fusion where each information source, was considered as a classifier. The second approach is a distance-based classification for belief functions modelling. Results of these approaches, on data collected on an urban axis in the South of France, show the outperformance of fusion strategies within this application.

Paper Details

Date Published: 18 April 2006
PDF: 16 pages
Proc. SPIE 6242, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006, 62420A (18 April 2006); doi: 10.1117/12.666745
Show Author Affiliations
N.-E. El Faouzi, Lab. d'Ingénierie Circulation Transports, INTRETS-ENTPE (France)
E. Lefevre, Lab. d'Informatique et d'Automatique de l'Artois, Univ. d'Artois (France)


Published in SPIE Proceedings Vol. 6242:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006
Belur V. Dasarathy, Editor(s)

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