Exploring Daily Tour Routes in Historical Peninsula by Using Generative Design

Yıl 2023, Cilt: 1 Sayı: 1, 27 - 37, 15.06.2023

Merve Deniz Tak Naime Hülya Berkmen

https://doi.org/10.26650/JTADP.01.006

Öz

In this study, with the help of a generative design, daily routes were created and optimized in light of certain objectives within the scope of the Historical Peninsula of Istanbul, especially in areas containing monumental structures and defined as world heritage sites. The current cultural-historical tours in the historical peninsula mainly focus on the main points such as Sultanahmet Square and its surroundings, Topkapı Palace and its surroundings, and the Grand Bazaar. However, within the scope of the peninsula, approximately 12,000 registered buildings (Istanbul Historic Peninsula Management Plan, 2018) are located on the Marmara Walls on the seven hills line, Land Walls, Golden Horn shores, and in each sub-region within its own urban texture. In this context, it is aimed to reveal the different route potentials within the protected areas that should be seen in the peninsula as a whole. In the study, road widths and topographical data which are two of the important parameters that constitute the original texture, and the areas that need to be seen at distances that can be visited daily were taken into consideration. In the whole historical peninsula, 9 different sightseeing routes were determined, the starting point of these routes and one mandatory stop for each route were defined, and the routes were optimized with single-objective optimization for walkability and exploration of the historical texture as much as possible. With this method, a decision support tool is provided for decision-makers such as city planners, tour guides, and travelers themselves.

Anahtar Kelimeler

İstanbul Historical Peninsula, generative design, single objective optimization, decision support tools

Kaynakça

• Alberti, M. (2008). Advances in Urban Ecology: Integrating Humans and Ecological Processes in Urban Ecosystems. Springer. doi.org/10.1007/978-0-387-75510-6 google scholar
• Batty, M., & Marshall, S. (2012). The origins of complexity theory in cities and planning. In complexity theories of cities have come of age: an overview with implications to urban planning and design 9783642446597. Springer. google scholar
• Chan, N. W., Samat, N., & Hoa, N. M. (2016). Decision Support System for Urban Planning. In Sustainable Urban Development Textbook. Water Watch Penang & Yokohama City University. google scholar
• Generative Design 101. (n.d.). Formlabs. Retrieved March 2, 2023, from https://formlabs.com/blog/generative-design/ google scholar
• Isaac Computer Science. (n.d.). A* search algorithm. Isaac Computer Science. Retrieved March 2, 2023, from https://isaaccomputerscience.org/concepts/dsa_search_a_star?examBoard=all&stage=all google scholar
• Istanbul Historic Peninsula Management Plan (2018). Istanbul Metropolitan Municipality.İstanbul. google scholar
• Küçükkoç, İ. (2020, 03 12). Optimizasyon Teknikleri Ders Notları. http://ikucukkoc.baun.edu.tr/lectures/EMM3208/EMM3208Birlestirilmis Notlar.pdf google scholar
• Lee, Y. (2015). The Parametric Design Genealogy of Zaha Hadid. Journal of Asian Architecture and Building Engineering, 14(2). 10.3130/jaabe.14.403 google scholar
• Portugali, J. (2018). What makes cities complex? http://www.spatialcomplexity.info/files/2013/10/Portugali.pdf google scholar
• Sarker, R. A., & Newton, C. S. (2007). Optimization Modelling: A Practical Approach. Taylor & Francis Group. https://doi.org/10.1201/9781420043112 google scholar
• Savic, D. (2002). Single-objective vs. Multiobjective Optimisation for Integrated Decision Support [International Congress on Environmental Modelling and Software] [International Congress on Environmental Modelling and Software]. google scholar
• Sugumaran, R., & Degroote, J. (2020). Spatial Decision Support Systems: Principles and Practices. Taylor & Francis Group. google scholar