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A Surrogate Function in Cellular GA for the Traffic Light Scheduling Problem

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Applications of Evolutionary Computation (EvoApplications 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13989))

Abstract

The traffic light scheduling problem is undoubtedly one of the most critical problems in a modern traffic management system. Appropriate traffic light planning can improve traffic flows, reduce vehicles’ emissions, and provide benefits for the whole city. Metaheuristics, notably the Cellular Genetic Algorithm (cGA), offer an alternative way of solving this optimization problem by providing “good solutions” to adjust the traffic lights to mitigate traffic congestion. However, one of the unresolved issues is these methods use very time-consuming operations. Specifically, the evaluation is a complex process since a simulator should be executed to get the quality of the solutions. In this work, we focus on this topic and propose using an artificial neural network (as a surrogate system) to tackle this problem. Our experiments show very promising results since our proposal can significantly reduce the execution time while maintaining (and even, in some scenarios, improving) the quality of the solutions.

This research is partially funded by the Universidad Nacional de la Patagonia Austral, the Universidad de Málaga, and the project PID 2020-116727RB-I00 (HUmove) funded by MCIN/AEI/10.13039/501100011033; and TAILOR ICT-48 Network (No. 952215) funded by EU Horizon 2020 research and innovation programme.

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Author information

Authors and Affiliations

  1. Universidad Nacional de la Patagonia Austral, Caleta Olivia, Argentina

    Andrea Villagra

  2. ITIS Software, Universidad de Málaga, Málaga, Spain

    Gabriel Luque

Authors
  1. Andrea Villagra
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  2. Gabriel Luque
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Correspondence to Andrea Villagra .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    João Correia

  2. University of York, York, UK

    Stephen Smith

  3. Al Hussein Technical University, Amman, Jordan

    Raneem Qaddoura

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Villagra, A., Luque, G. (2023). A Surrogate Function in Cellular GA for the Traffic Light Scheduling Problem. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_50

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  • DOI: https://doi.org/10.1007/978-3-031-30229-9_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30228-2

  • Online ISBN: 978-3-031-30229-9

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