WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

Browse

Filters

2004 - 200912010 - 20161

Settings

Author: search.filters.author.Çelik, Hüseyin MuratSubject: search.filters.subject.Artificial neural networks

Search Results

Now showing 1 - 2 of 2
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Soft Computing and Regression Modelling Approaches for Link-Capacity Functions
    (Czech Technical University in Prague, 2016) Koşun, Çağlar; Tayfur, Gökmen; Çelik, Hüseyin Murat; Tayfur, Gökmen; 02.03. Department of City and Regional Planning; 03.03. Department of Civil Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology; 02. Faculty of Architecture
    Link-capacity functions are the relationships between the fundamental traffic variables like travel time and the flow rate. These relationships are important inputs to the capacity-restrained traffic assignment models. This study investigates the prediction of travel time as a function of several variables V/C (flow rate/capacity), retail activity, parking, number of bus stops and link type. For this purpose, the necessary data collected in Izmir, Turkey are employed by Artificial Neural Networks (ANNs) and Regression-based models of multiple linear regression (MLR) and multiple non-linear regression (MNLR). In ANNs modelling, 70% of the whole dataset is randomly selected for the training, whereas the rest is utilized in testing the model. Similarly, the same training dataset is employed in obtaining the optimal values of the coefficients of the regression-based models. Although all of the variables are used in the input vector of the models to predict the travel time, the most significant independent variables are found to be V/C and retail activity. By considering these two significant input variables, ANNs predicted the travel time with the correlation coefficient R = 0:87 while this value was almost 0.60 for the regression-based models.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 8
    Forecasting Interregional Commodity Flows Using Artificial Neural Networks: an Evaluation
    (Taylor and Francis Ltd., 2004) Çelik, Hüseyin Murat; Çelik, Hüseyin Murat; 02.03. Department of City and Regional Planning; 02. Faculty of Architecture; 01. Izmir Institute of Technology
    Previous 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.