Abstract—This paper provides an agent technology for a decision support system. This system is designed to detect and regulate the traffic of multimodal public transport when many disturbances come simultaneously. The objective of this system is to optimize the regulation action by learning technique of regulator. The goal of this research is to improve the quality of public transport service provided to users and respect the use rules (safety rules, business rules, commercial rules, etc.). So, to improve the quality service of the user, we have to optimize simultaneously several criteria like punctuality, regularity and correspondence in disturbance case. In this paper, we focus primarily on a multi agent system for optimizing and learning of Regulation Support System of a Multimodal Public Transport (RSSPT). We have validated our strategy by simulating situation related to existing transportation system.
Index Terms—Multi agent system, learning agent, optimization, regulation support system, intelligent transportation system.
Nabil Morri is with the Emirates College of Technology and Tunis University, Tunisia (e-mail: morrynabil@ gmail.com).
Sameh Hadouaj is with Higher Colleges of Technology of Abu Dhabi and Tunis University, Tunisia (e-mail: hadouaj@yahoo.fr).
Lamjed Ben Said is with Tunis University, Tunisia (e-mail: lamjed.bensaid@isg.rnu.tn).
Cite: Nabil Morri, Sameh Hadouaj, and Lamjed Ben Said, "Agent Technology for Multi-criteria Regulation in Public Transportation," International Journal of Machine Learning and Computing vol.6, no. 2, pp. 105-110, 2016.