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 |
Show full item record
CORE Recommender
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.