City and Regional Planning / Şehir ve Bölge Planlama

Permanent URI for this collectionhttps://hdl.handle.net/11147/4274

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Publisher: search.filters.publisher.Czech Technical University in PragueScopus Q: search.filters.scopusQuartile.Q4

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  • 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
    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: 2
    Citation - Scopus: 1
    Significance of Rent Attributes in Prediction of Earthquake Damage in Adapazari, Turkey
    (Czech Technical University in Prague, 2014) Tayfur, Gökmen; Bektaş, Birkan; Duvarcı, Yavuz
    This paper analyses rent-based determinants of earthquake damage from an urban planning perspective with the data gathered from Adapazari, Turkey, after the disaster in 1999 Eastern Marmara Earthquake (EME). The study employs linear regression, log-linear regression, and artificial neural networks (ANN) methods for cross-verification of results and for finding out the significant urban rent attribute(s) responsible for the damage. All models used are equally capable of predicting the earthquake damage and converge to similar results even if the data are limited. Of the rent variables, the physical density is proved to be especially significant in predicting earthquake damage, while the land value contributes to building resistance. Thus, urban rent can be the primary tool for planners to help reduce the fatalities in preventive planning studies.