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Comparative Analysis of Selected Algorithms in the Process of Optimization of Traffic Lights

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10192))

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Abstract

Optimal settings of traffic lights and traffic light cycles are important tasks of modeling a modern ordered traffic in smart cities. This article analyzes the comparative effectiveness of selected optimization algorithms for the identified area. In particular, it involves the comparison of the concepts of genetic algorithm using particle swarm optimization, the differential evolution and the Monte Carlo method with two new approaches: evolution strategy involving the adaptation of the covariance matrix and topology archipelago consisting of four islands - different algorithms to optimize the length of the phase in fixed time traffic signals. Developed simulation solutions allowed to achieve a quantitative improvement in the selection of the optimal durations of the phases of traffic lights for the tested roads with junctions.

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Authors and Affiliations

  1. Faculty of Computer Science, West Pomeranian University of Technology, Szczecin, Poland

    K. Małecki & P. Pietruszka

  2. Maritime University of Szczecin, Szczecin, Poland

    S. Iwan

Authors
  1. K. Małecki
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  2. P. Pietruszka
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  3. S. Iwan
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Corresponding author

Correspondence to K. Małecki .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology, Wroclaw, Poland

    Bogdan Trawiński

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Małecki, K., Pietruszka, P., Iwan, S. (2017). Comparative Analysis of Selected Algorithms in the Process of Optimization of Traffic Lights. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_48

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  • DOI: https://doi.org/10.1007/978-3-319-54430-4_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54429-8

  • Online ISBN: 978-3-319-54430-4

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