Yapay zeka yöntemleri kullanarak toplu taşımada Antalya için optimum hat tasarımı

Public transportation is one of the most important services for cities. The growth of the cities constantly increases the passenger demand and the covered area for the public transportation. Increase in demand and coverage causes discomfort and high cost of service. Thus, the public transportation system has to be updated in most efficient and balanced way. With advancing data collecting and big data technologies, it is now possible to research more comprehensive optimization methods for public transportation planning. The methods introduced in this thesis can be helpful in various public transportation planning decisions regarding mostly bus lines such as Frequency Setting Problem. The aim of Boarding Pattern Clustering is clustering the bus lines with similar demand patterns through a regular day regardless of its popularity. Bus lines in same clusters can be considered together in various plannings. In Time Slot Clustering, various time slots that similar demands are determined and each time slot can have its own frequency setting to balance cost and comfort more dynamically. Alighting counts and bus occupancies of a route are estimated with probabilistic approach by using the data belongs to its reverse route.