NDLI: An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach

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An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach

Content Provider	Scilit
Author	Yan, Fei Dridi, Mahjoub Moudni, Abdellah El
Copyright Year	2013
Description	This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.
Related Links	https://content.sciendo.com/downloadpdf/journals/amcs/23/1/article-p183.pdf
ISSN	1641876X
DOI	10.2478/amcs-2013-0015
Journal	International Journal of Applied Mathematics and Computer Science
Issue Number	1
Volume Number	23
Language	English
Publisher	Walter de Gruyter GmbH
Publisher Date	2013-03-01
Access Restriction	Open
Subject Keyword	Journal: International Journal of Applied Mathematics and Computer Science International Journal of Applied Mathematics and Computer Science Operations Research and Management Science Autonomous Intersection Management Autonomous Vehicles Dynamic Programming Genetic Algorithm
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Computer Science Engineering

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