4.6 Article

Optimizing urban traffic flow using Genetic Algorithm with Petri net analysis as fitness function

期刊

NEUROCOMPUTING
卷 124, 期 -, 页码 162-167

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ELSEVIER
DOI: 10.1016/j.neucom.2013.07.015

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Urban traffic; Genetic Algorithm; Petri net; Optimization

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This paper describes a new methodology adopted for urban traffic stream optimization. By using Petri net analysis as fitness function of a Genetic Algorithm, an entire urban road network is controlled in real time. With the advent of new technologies that have been published, particularly focusing on communications among vehicles and roads infrastructures, we consider that vehicles can provide their positions and their destinations to a central server so that it is able to calculate the best route for one of them. Our tests concentrate on comparisons between the proposed approach and other algorithms that are currently used for the same purpose, being possible to conclude that our algorithm optimizes traffic in a relevant manner. (C) 2013 Elsevier B.V. All rights reserved.

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