Abstract
Urban mobility is a major challenge in modern societies. Increasing the infrastructure’s physical capacity has proven to be unsustainable from a socio-economical perspective. Intelligent transportation systems (ITS) emerge in this context, aiming to make a more efficient use of existing road networks by means of new technologies. In this paper we address the route choice problem, in which drivers need to decide which route to take to reach their destinations. In this respect, we model the problem as a multiagent system where each driver is represented by a learning automaton, and learns to choose routes based on past experiences. In order to improve the learning process, we also propose a mechanism that updates the drivers’ set of routes, allowing faster routes to be learned. We show that our approach provides reasonably good solutions, and is able to mitigate congestion levels in main roads.
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de O. Ramos, G., Grunitzki, R. (2015). An Improved Learning Automata Approach for the Route Choice Problem. In: Koch, F., Meneguzzi, F., Lakkaraju, K. (eds) Agent Technology for Intelligent Mobile Services and Smart Societies. AVSA CARE 2014 2014. Communications in Computer and Information Science, vol 498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46241-6_6
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DOI: https://doi.org/10.1007/978-3-662-46241-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46240-9
Online ISBN: 978-3-662-46241-6
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