Citation

Faro A, Giordano D, Spampinato C. IEEE Trans. Neural. Netw. 2008; 19(6): 1108-1129.

Affiliation

Department of Informatics and Telecommunication Engineering, University of Catania, Sicily 95125, Italy. afaro@diit.unict.it

Copyright

(Copyright © 2008, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TNN.2008.2000392

PMID

18541508

Abstract

This paper proposes a traffic monitoring architecture based on a high-speed communication network whose nodes are equipped with fuzzy processors and cellular neural network (CNN) embedded systems. It implements a real-time mobility information system where visual human perceptions sent by people working on the territory and video-sequences of traffic taken from webcams are jointly processed to evaluate the fundamental traffic parameters for every street of a metropolitan area. This paper presents the whole methodology for data collection and analysis and compares the accuracy and the processing time of the proposed soft computing techniques with other existing algorithms. Moreover, this paper discusses when and why it is recommended to fuse the visual perceptions of the traffic with the automated measurements taken from the webcams to compute the maximum traveling time that is likely needed to reach any destination in the traffic network.

Language: en