Çevık ve optımum ulas ̧ım sıstemı olus ̧turmak

In today's world, increased and wide-spread population have increased the demand for public transportation. This study takes stop density, stop layout and passenger population of those stops into consideration and offers a better regulated public transportation net- work design that can satisfy the increased demand. In this study, boarding data is provided by the company that is in charge of Antalya's public transportation system. Remaining inputs are automatically taken from company's API service using .NETCore command line application and saved into a PostgreSQL database that is hosted on Azure. Google Colab, a Jupyter Notebook service, is then used as the development environment to pro- cess the data using Python language. After visualizing inputs such as bus routes, stop layout and passenger density on Google Maps and KeplerGL, with the use of DBSCAN and K-Means algorithms, data is clustered and a new way of connecting clusters is offered as a result of Uniform Cost Search. In the cost function, shortening the distance between clusters is assumed as a cost amplifier and increasing the passenger count is assumed to lower the cost. A good solution respect to cost function found with genetic algorithm from all triple, quadruple and quintet permutations of routes.