City and Regional Planning / Şehir ve Bölge Planlama
Permanent URI for this collectionhttps://hdl.handle.net/11147/4274
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Article Citation - WoS: 2Citation - Scopus: 1Significance of Rent Attributes in Prediction of Earthquake Damage in Adapazari, Turkey(Czech Technical University in Prague, 2014) Tayfur, Gökmen; Bektaş, Birkan; Duvarcı, YavuzThis 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.Conference Object A Data Coding and Screening System for Accident Risk Patterns: A Learning System(WITPress, 2011) Geçer Sargın, Feral; Geçer Sargın, Feral; Duvarcı, Yavuz; Duvarcı, Yavuz; İnan, E.; İnan, E.; Kumova, Bora İsmail; Kumova, Bora İsmail; Atay Kaya, İlgi; Atay Kaya, İlgiAccidents on urban roads can occur for many reasons, and the contributing factors together pose some complexity in the analysis of the casualties. In order to simplify the analysis and track changes from one accident to another for comparability, an authentic data coding and category analysis methods are developed, leading to data mining rules. To deal with a huge number of parameters, first, most qualitative data are converted into categorical codes (alpha-numeric), so that computing capacity would also be increased. Second, the whole data entry per accident are turned into ID codes, meaning each crash is possibly unique in attributes, called 'accident combination', reducing the large number of similar value accident records into smaller sets of data. This genetical code technique allows us to learn accident types with its solid attributes. The learning (output averages) provides a decision support mechanism for taking necessary cautions for similar combinations. The results can be analyzed by inputs, outputs (attributes), time (years) and the space (streets). According to Izmir's case results; sampled data and its accident combinations are obtained for 3 years (2005 - 2007) and their attributes are learned. © 2011 WIT Press.Conference Object Citation - WoS: 1Citation - Scopus: 1The Method of Policy Capturing for the Transportation Disadvantaged: Simulation Results(WITPress, 2003) Duvarcı, Yavuz; Gür, GüneşIn the previous study called "A Modelling Approach for the Transportation Disadvantaged", which was an experimental one calibrated in a small town in Turkey, it was observed that an integrated TPM for the disadvantaged category was probable, and the findings were observable at all stages of the sequential modelling, however, with slight differences compared to the Normal model's results. Following the previous one, this study shows the method of how "policy capturing" could be possible on the basis of these differences, which aims to help improve the adverse conditions of the disadvantaged. The method is sort of category analysis based on the cluster analysis results, since it is clearly verified that the "disadvantage indices" identified as the single-disadvantage groups match with the values of cluster centres. Using TRANUS software, three simulations are run for three dimensions of disadvantage: socio-economic (categorical), spatial and the positional. The simulation results, evaluated from different criteria, showed that socio-economic dimension was the most fruitful area for policy capturing.