Latest revision as of 13:46, 12 February 2021

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 26.9× when compared with the sequential version. Keywords: Bus Rapid Transit, Traffic lights synchronization, Evolutionary algorithm

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• http://dx.doi.org/10.1016/j.ijtst.2018.10.002 under the license http://creativecommons.org/licenses/by-nc-nd/4.0

• http://orca.cf.ac.uk/115734/9/main.pdf under the license http://creativecommons.org/licenses/by-nc-nd/4.0

• https://doi.org/10.1016/j.ijtst.2018.10.002 under the license http://creativecommons.org/licenses/by-nc-nd/4.0

• http://www.sciencedirect.com/science/article/pii/S2046043018300339,

https://doaj.org/toc/2046-0430 under the license cc-by-nc-nd

• https://api.elsevier.com/content/article/PII:S2046043018300339?httpAccept=text/xml,

https://api.elsevier.com/content/article/PII:S2046043018300339?httpAccept=text/plain,

http://dx.doi.org/10.1016/j.ijtst.2018.10.002

• https://www.sciencedirect.com/science/article/pii/S2046043018300339,

http://orca.cf.ac.uk/115734,

http://orca-mwe.cf.ac.uk/115734,

https://academic.microsoft.com/#/detail/2896179998 under the license https://www.elsevier.com/tdm/userlicense/1.0/