1. Modelling trip distribution with fuzzy and genetic fuzzy systems
- Author
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Mert Kompil, H. Murat Celik, Kompil, Mert, Çelik, Hüseyin Murat, and Izmir Institute of Technology. City and Regional Planning
- Subjects
Adaptive neuro fuzzy inference system ,Engineering ,Fuzzy classification ,Fuzzy rule ,Neuro-fuzzy ,business.industry ,Trip distribution ,Geography, Planning and Development ,Transportation ,Genetic algorithms ,Learning algorithms ,Machine learning ,computer.software_genre ,Defuzzification ,Fuzzy logic ,Genetic fuzzy systems ,Spatial interaction models ,Fuzzy number ,Fuzzy set operations ,Artificial intelligence ,Data mining ,business ,computer ,Neural networks - 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.J.1-Economics of Climate Change, Energy and Transport
- Published
- 2013