Agent-Based Traffic Control: a Fuzzy Q-Learning Approach

• Type: Article in International Conference Proceedings Book
• Date: 2010

Authors

Juan C. Pacheco (Author) FEUP; Rosaldo J. F. Rossetti (Author) FEUP

Conference proceedings International

Title: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Pages: 1172-1177

13th International IEEE Conference on Intelligent Transportation Systems, ITSC 2010

Funchal, 19 September 2010 through 22 September 2010

Indexing

ISI Proceedings

Scopus - 2 Citations

INSPEC

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-007-WRG

DOI: 10.1109/itsc.2010.5624984

Abstract (EN): Network modelling and simulation is an important approach to implement effective traffic control strategies and management policies for urban mobility. With much advance in communication and information technologies, and as ubiquitous computing becomes part of most people's lives, both infrastructure and user experiment a new dimension of their relationship that demands for a proper behaviour from control and management systems. In this paper we use the autonomous agent metaphor to implement intelligent control through a fuzzy Q-learning approach. The controller decision-making mechanisms and some preliminary simulation experiments are set up so as to assess the feasibility of our model to cope with urban traffic coordination and management. ©2010 IEEE.

Language: English

Type (Professor's evaluation): Scientific