Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5335
Title: Modelling trip distribution with fuzzy and genetic fuzzy systems
Authors: Kompil, Mert
Çelik, Hüseyin Murat
Keywords: Spatial interaction models
Fuzzy logic
Genetic algorithms
Trip distribution
Learning algorithms
Neural networks
Issue Date: Mar-2013
Publisher: Taylor and Francis Ltd.
Source: Kompil, M., and Çelik, H.M. (2013). Modelling trip distribution with fuzzy and genetic fuzzy systems. Transportation Planning and Technology, 36(2), 170-200. doi:10.1080/03081060.2013.770946
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.
URI: http://doi.org/10.1080/03081060.2013.770946
http://hdl.handle.net/11147/5335
ISSN: 0308-1060
0308-1060
1029-0354
Appears in Collections:City and Regional Planning / Şehir ve Bölge Planlama
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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