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Intelligent transportation systems use parameters such as traffic flow, density and speed to manage city traffic. This paper presents a novel prediction model for traffic speed prediction consisting of nine stages. In the presented model, real vehicle data were passed through data filtering and map matching processes, density-based clusters were created, cluster features were generated, instant traffic state was displayed, and traffic speed prediction was performed using with the artificial neural network RNN model. In previous studies, while traffic speed prediction can be performed on a specific road with stationary data sources or on different days with distributed GPS records, in the developed model, characteristics of the interested road are created by obtaining density-based vehicle cluster features, short-term and data-driven speed prediction is made within the changeable structure of traffic. Speed prediction was tested on Eskisehir and Istanbul roads belonging to Ankara province, the error rates were determined for speed prediction using the RNN variant LSTM and GRU methods, Eskisehir road LSTM-GRU error rates were measured as 8,595-8,656 and İstanbul road error rates as 7,331-7,955, respectively. The developed model for the changeable nature of traffic has yielded successful results in near real time. It is considered that the proposed model will offer different and new solutions in the prediction of traffic parameters, accelerate the processes and assist to the users more accurate and faster services. [ABSTRACT FROM AUTHOR]

Buildings - especially the residential buildings - are among the high priority areas in climate change due to their large share of CO2 emissions, their significant energy saving potentials and the increased comfort expectations of their occupants. A systematic and numerical investigation of how building performance will be affected by climate change in the future is crucial for both retrofitting the existing buildings and making long-term strategical decisions. This paper presents the simulation-based results of the climate change impact assessment for a typical multi-storey residential building in Istanbul. In this study, RCP4.5 and RCP8.5 scenarios - representing the low/medium and high CO2 emission scenarios - defined by the Intergovernmental Panel on Climate Change (IPCC) are used to generate two different climate data sets for 2060. Three cooling scenarios (natural ventilation, hybrid ventilation and air-conditioned) have been developed to represent different building usage profiles for the building simulations. Investigated performance metrics were selected as building heating/cooling energy consumption, CO2 emission and occupant thermal comfort. Analysis results analysis show that the predicted temperature rise will have a strong impact on the building cooling energy consumption and/or occupant thermal comfort. The results indicate the necessity of taking measures for adaptation of buildings to the changing climate and the importance of decarbonization of electricity generation to mitigate the effects of climate change. This study will also serve as a basis for future retrofit studies for the changing climate. [ABSTRACT FROM AUTHOR]

The historical town of Elmalı, situated on the Taurus Mountains near Antalya, attracts attention by its wellpreserved urban establishment. This paper considers the Urban Conservation Project of the Historical Site of Elmalı Town that was formulated in the Spring Term of 2005-2006 Academic Year at the Graduate Program in Restoration at Istanbul Technical University. An intensive research on the historical, cultural and architectural background of Elmalı was carried out in order to understand the relations of the physical environment of the site and the urban identity. The collaborative observations on the site and the analysis of the site based on certain categories were then developed into proposals to rehabilitate the site. [ABSTRACT FROM AUTHOR]