Parseh Designers Transportation Research Institute Quarterly Journal of Transportation Engineering 2008-6598 17 2 2025 12 22 Discovering Spatio-Temporal Travel Patterns in Urban Public Transportation: Insights from Smart Card Data Analysis Discovering Spatio-Temporal Travel Patterns in Urban Public Transportation: Insights from Smart Card Data Analysis 5373 5393 228622 10.22119/jte.2025.508176.2733 FA Shariat Radfar Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran Hamidreza Koosha Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran Ali Gholami Department of Civil Engineering, Faculty of Engineering, Golestan University, Gorgan, Iran Atefeh Amindoust Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran Journal Article 2025 02 22 Public transportation systems exhibit complex travel behaviors influenced by spatial and temporal factors such as passenger origins, time-dependent demand, and urban structural characteristics. Understanding these behaviors is essential for improving transportation planning and urban development. This study investigates zone-based travel trends in the city of Mashhad, Iran, using smart card data collected from bus and metro systems. Temporal travel patterns of passengers across 253 traffic zones were classified into three categories—morning, noon, and evening—using the K-means clustering method. Meanwhile, the Mean Shift clustering method was employed to examine spatial characteristics such as population distribution and urban development within each zone. The results reveal distinct clusters for both temporal and spatial dimensions, highlighting the complex relationships among travel trends, demographic factors, and land-use characteristics. Key findings include a strong association between residential areas and morning trips, commercial and educational centers with midday trips, and the connectivity of peripheral areas with adjacent residential neighborhoods. These results provide practical insights for urban planners and policymakers to improve transportation systems and land-use policies. Public transportation systems exhibit complex travel behaviors influenced by spatial and temporal factors such as passenger origins, time-dependent demand, and urban structural characteristics. Understanding these behaviors is essential for improving transportation planning and urban development. This study investigates zone-based travel trends in the city of Mashhad, Iran, using smart card data collected from bus and metro systems. Temporal travel patterns of passengers across 253 traffic zones were classified into three categories—morning, noon, and evening—using the K-means clustering method. Meanwhile, the Mean Shift clustering method was employed to examine spatial characteristics such as population distribution and urban development within each zone. The results reveal distinct clusters for both temporal and spatial dimensions, highlighting the complex relationships among travel trends, demographic factors, and land-use characteristics. Key findings include a strong association between residential areas and morning trips, commercial and educational centers with midday trips, and the connectivity of peripheral areas with adjacent residential neighborhoods. These results provide practical insights for urban planners and policymakers to improve transportation systems and land-use policies. https://jte.sinaweb.net/article_228622_b40d0c3499a6ba7447e8170bb5b876e9.pdf