Please use this identifier to cite or link to this item:
|Title:||Modelling trip distribution with fuzzy and genetic fuzzy systems|
|Authors:||KOMPIL MERT; CELIK H. Murat|
|Citation:||TRANSPORTATION PLANNING AND TECHNOLOGY vol. 36 no. 2 p. 170-200|
|Publisher:||TAYLOR & FRANCIS LTD|
|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|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.