Investigating public transit daily travel behavior using smart card data: a case study of Konya

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Thesis_MAJED_ALKRDY_MA_oct04.pdf

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2022-9

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AL KRDY, MAJED

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As urban diversity grows in cities, so does the heterogeneity of public transit (PT) travel behavior. To accommodate for usage heterogeneity, a deeper understanding of travel behavior beyond descriptive analysis is necessary, which can be performed using smart card data (SCD), if available. In this study, K-means clustering algorithm combined with a data mining technique that includes unsupervised learning clustering algorithm is proposed to detect different demand segments among PT users based on their daily boarding activity from the SCD. The numerical results are obtained for the PT usage in the city of Konya, Turkey. Descriptive statistics of PT behavior from the SCD serve as an introductory analysis, while daily travel patterns at user levels are searched for detection of various travel patterns, which can be used by local authorities to improve the PT services and their customization for local demand.

Subject Keywords

Public Transit, Clustering, Unsupervised Learning Algorithm, Descriptive statistics, Smart Card Data

URI

https://hdl.handle.net/11511/99781

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

M. AL KRDY, “Investigating public transit daily travel behavior using smart card data: a case study of Konya,” M.S. - Master of Science, Middle East Technical University, 2022.