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Modelling trip distribution with fuzzy and genetic fuzzy systems

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  • Mert Kompil
  • H. Murat Celik

Abstract

This paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity- and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost.

Suggested Citation

  • Mert Kompil & H. Murat Celik, 2013. "Modelling trip distribution with fuzzy and genetic fuzzy systems," Transportation Planning and Technology, Taylor & Francis Journals, vol. 36(2), pages 170-200, April.
    • Handle: RePEc:taf:transp:v:36:y:2013:i:2:p:170-200
    DOI: 10.1080/03081060.2013.770946
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    Cited by:

    1. Javier Rubio-Herrero & Jesús Muñuzuri, 2021. "Indirect estimation of interregional freight flows with a real-valued genetic algorithm," Transportation, Springer, vol. 48(1), pages 257-282, February.
    2. Javier Rubio-Herrero & Jesús Muñuzuri, 2023. "Sparse regression for data-driven deterrence functions in gravity models," Annals of Operations Research, Springer, vol. 323(1), pages 153-174, April.

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