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Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm

Nesmachnow, Sergio, Massobrio, Renzo, Arreche, Efraín and Mumford, Christine 2019. Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm. International Journal of Transportation Science and Technology 8 (1) , pp. 53-67. 10.1016/j.ijtst.2018.10.002

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Abstract

This article presents a parallel evolutionary algorithm for public transport optimization by synchronizing traffic lights in the context of Bus Rapid Transit systems. The related optimization problem is NP-hard, so exact computational methods are not useful to solve real-world instances. Our research introduces a parallel evolutionary algorithm to efficiently configure and synchronize traffic lights and improve the average speed of buses and other vehicles. The Bus Rapid Transit on Garzón Avenue (Montevideo, Uruguay) is used as a case study. This is an interesting complex urban scenario due to the number of crossings, streets, and traffic lights in the zone. The experimental analysis compares the numerical results computed by the parallel evolutionary algorithm with a scenario that models the current reality. The results show that the proposed evolutionary algorithm achieves better quality of service when compared with the current reality, improving up to 15.3% the average bus speed and 24.8% the average speed of other vehicles. A multiobjective optimization analysis also demonstrates that additional improvements can be achieved by assigning different priorities to buses and other vehicles. In addition, further improvements can be achieved on a modified scenario simply by deleting a few bus stops and changing some traffic lights rules. The benefits of using a parallel solver are also highlighted, as the parallel version is able to accelerate the execution times up to 26.9x when compared with the sequential version.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA76 Computer software
Publisher: Multi-Science Publishing / Elsevier
ISSN: 2046-0430
Date of First Compliant Deposit: 11 October 2018
Date of Acceptance: 5 October 2018
Last Modified: 20 May 2019 11:22
URI: http://orca.cf.ac.uk/id/eprint/115734

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