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Clustering on smart card data may provide direct information about passenger travel patterns. Services could be adjusted according to demands that change over time to optimize the public transport system. This study investigates three possible clustering methods that can be used to classify average daily boarding patterns of public transportation lines that are represented as time series. In literature, similar studies exist but they characterize arrivals of passengers to a single stop throughout a day. In this study, all bus lines from departure to arrival are investigated for all bus stops throughout a whole day. We have found out that important time intervals might be generalized as dominant peak or dip times. In addition, bus lines with similar peak load rates or similar up and down trends may be clustered depending on the planning purpose. No sponsor

Transit authorities have been searching for the indicators to measure transit service quality and the key factors to attract citizens who do not prefer public transport. The recent advent of data collection technologies such as AVL, APC, GPS and Smart Card (SC) promise opportunities for conducting comprehensive transit system performance measures, improving the quality of service while meeting passenger needs and reducing operation costs. The aim of this study is to propose metrics to improve transit service. The focus is on bus transportation since it is more flexible compared to rail transportation and widely preferred by the masses in cities. The primary data source of this study comes from the Department of Transportation for the City of Antalya. We load the complete boarding data of December 18,2019 which is a standard weekday. Most of the analysis is done using Knime software. As an outcome of this research, the analysis may propose to modify or eliminate inefficient routes, suggest new lines, identify inefficient bus stops, and potentially modify path of a route. No sponsor