Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 9Citation - Scopus: 10Modelling Trip Distribution With Fuzzy and Genetic Fuzzy Systems(Taylor and Francis Ltd., 2013) Kompil, Mert; Çelik, Hüseyin MuratThis 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.Article Citation - Scopus: 31Spatial Interaction Modeling of Interregional Commodity Flows(Elsevier Ltd., 2007) Çelik, Hüseyin Murat; Guldmann, Jean-MichelDrawing from both the spatial price equilibrium theoretical framework and the empirical literature on spatial interaction modeling, this paper expands models of interregional commodity flows (CFs) by incorporating new variables and using a flexible Box-Cox functional form. The 1993 US commodity flows survey provides the empirical basis for estimating state-to-state flow models for 16 commodity groups over the 48 continental US states. The optimized Box-Cox specification proves to be superior to the multiplicative one in all cases, while selected variables provide new insights into the determinants of state-to-state CFs.Article Citation - Scopus: 33Modeling Freight Distribution Using Artificial Neural Networks(Elsevier Ltd., 2004) Çelik, Hüseyin MuratStudies about freight distribution modeling are limited due to the limitations in data availability. Existing studies in this subject, generally either use the conventional gravity models or the regression based models as modeling techniques. The present study, using the 1993 US Commodity Flow Survey Data, models inter-regional commodity flows for 48 continental states of the US with three different artificial neural networks (ANN). The results are compared with those of Celik and Guldmann's (2002) Box-Cox Regression Model. The ANN using conventional gravity model variables provides a slight improvement with respect to this Box-Cox model. However, the ANNs using theoretically relevant variables provide surprising improvements in comparison to the Box-Cox model. It is concluded that ANN architecture is a very promising technique for predicting short-term inter-regional commodity flows.Article Citation - WoS: 5Citation - Scopus: 8Forecasting Interregional Commodity Flows Using Artificial Neural Networks: an Evaluation(Taylor and Francis Ltd., 2004) Çelik, Hüseyin MuratPrevious studies have concluded that the use of artificial neural networks (ANNs) is a promising new technique for modelling freight distribution, supporting, the findings of other studies in the area of spatial interaction modelling. However, the forecasting performance of ANNs is still under investigation. This study tests the predictive performance of the ANN Model with respect to a Box-Cox spatial interaction model. It is concluded that the Box-Cox model outperforms ANN in forecasting interregional commodity flows even if ANN had proven calibration superiority in comparison to conventional gravity type models.