Date of Publication :31st July 2024

Abstract:Data mining can be defined as the extraction of unclear and previously unknown but potentially usable information and patterns from a large dataset. This technique allows for uncovering relationships among existing data and making predictions for the future when necessary. This study aims to analyse the data, by utilizing related information of individuals, obtained from transportation master plan survey studies of city of Sakarya, Türkiye. Through the apriori algorithm from data mining program WEKA, the relationships and rules were obtained for the events that are likely to occur together. In this regard, the daily transportation and trip related data of 9876 individuals were identified and listed. The results revealed the impact of the variables such as age group, gender, education, employment status, driver's license ownership, household income, and type of vehicle on the number of trips done by the people in the study. As being one of the data mining techniques, association rule analysis was applied to the data from the survey conducted in order to obtain the related rules and probabilities generated. Such studies are likely to be further developed to lead more accurate and widespread assessments in decisions related to transportation planning.

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