Title: Modelling trip distribution with fuzzy and genetic fuzzy systems
Citation: TRANSPORTATION PLANNING AND TECHNOLOGY vol. 36 no. 2 p. 170-200
Publication Year: 2013
JRC N°: JRC80266
ISSN: 0308-1060
URI: http://www.tandfonline.com/doi/abs/10.1080/03081060.2013.770946
DOI: 10.1080/03081060.2013.770946
Type: Articles in periodicals and books
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.
JRC Directorate:Growth and Innovation

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