Modelling trip distribution with fuzzy and genetic fuzzy systems

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 lear...

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Bibliographic Details
Published in:Transportation planning and technology 2013-03, Vol.36 (2), p.170-200
Main Authors: Kompil, Mert, Celik, H. Murat
Format: Article
Language:English
Subjects:
Fuzzy
Fuzzy logic
fuzzy rule-based systems
Fuzzy set theory
Fuzzy systems
Genetic algorithms
genetic fuzzy systems
Genetics
Learning
Modelling
Neural networks
spatial interaction models
Transportation planning
trip distribution
Urban areas
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Summary: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.
ISSN:0308-1060
1029-0354
DOI:10.1080/03081060.2013.770946