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Signal Setting Design at a Single Junction Through the Application of Genetic Algorithms

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Computer-based Modelling and Optimization in Transportation

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 262))

Abstract

The purpose of this chapter is the application of Genetic Algorithms to solve the Signal Setting Design at a single junction. Two methods are compared: the monocriteria and the multicriteria optimisations. In the former case, three different objectives functions were considered: the capacity factor maximisation, the total delay minimisation and the total number of stops minimisation; in the latter case, two combinations of criteria were investigated: the total delay minimisation and the capacity factor maximisation, the total delay minimisation and the total number of stops minimisation. Furthermore, two multicriteria genetic algorithms were compared: the Goldberg’s Pareto Ranking (GPR) and the Non Dominated Sorting Genetic Algorithms (NSGA-II). Conclusions discuss the effectiveness of multicriteria optimisation with respect to monocriteria optimisation, and the effectiveness of NSGA-II with respect to the GPR.

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Authors and Affiliations

  1. Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084, Fisciano, SA, Italy

    Giulio Erberto Cantarella, Stefano de Luca, Roberta Di Pace & Silvio Memoli

Authors
  1. Giulio Erberto Cantarella
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  2. Stefano de Luca
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  3. Roberta Di Pace
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  4. Silvio Memoli
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Corresponding author

Correspondence to Stefano de Luca .

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Editors and Affiliations

  1. UGEI-INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal

    Jorge Freire de Sousa

  2. Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, Italy

    Riccardo Rossi

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Cantarella, G.E., de Luca, S., Di Pace, R., Memoli, S. (2014). Signal Setting Design at a Single Junction Through the Application of Genetic Algorithms. In: de Sousa, J., Rossi, R. (eds) Computer-based Modelling and Optimization in Transportation. Advances in Intelligent Systems and Computing, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-319-04630-3_24

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  • DOI: https://doi.org/10.1007/978-3-319-04630-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04629-7

  • Online ISBN: 978-3-319-04630-3

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