Dosdoğru, Ayşe Tuğbaİpek, Aslı BoruGöçken, MustafaÖzçalıcı, Mehmet2025-01-062025-01-0620221304-88801304-889910.35379/cusosbil.1030398https://doi.org/10.35379/cusosbil.1030398https://search.trdizin.gov.tr/tr/yayin/detay/1105159https://hdl.handle.net/20.500.14669/771Fast, reliable and comfortable transportation of people increases the level of livability in cities. It also influences people's quality of life. Therefore, researches are needed to improve transportation services. Various models are developed to analyze the transportation services but each of which has its own advantages and disadvantages. Today, companies collect large amounts of data to improve their service quality. To survive in competition environment, they must use the collected data in order to create value for their customers and employees. There are many factors that affect the transportation services. Therefore, it is difficult to solve the problems in transportation services using classical methods. The main goal of our study is to determine the bus ticket price accurately. In this study, k-means algorithm, which is popular because of its simplicity and versatility, is firstly used to discover more meaningful information. Then the price, which is one of the most important elements of passenger transportation, is forecasted using six different forecasting model including linear regression, support vector regression, regression tree, gaussian process regression, genetic algorithm based artificial neural network, and ensemble model. The results of this study showed that proposed forecasting models can meet expectations in dynamic environmental conditions.eninfo:eu-repo/semantics/openAccessSupport vector regressionLinear regressionK-means algorithmGenetic algorithm based artificial neural networkTicket price forecastingArticle1441133110515931