Travel patterns in public transportation systems are shaped by a complex interplay of spatial and temporal factors, including passenger location, time of day, and urban infrastructure. Unraveling these patterns is crucial for effective planning and development. This study delves into zonal-based public transport travel behavior in Mashhad, Iran, leveraging smart card data from bus and metro systems. K-means clustering unveils distinct temporal patterns (morning, noon, evening) in passenger trips across 253 traffic zones. Meanwhile, Mean Shift clustering explores the spatial dimension, analyzing population density and built-up areas within each zone. The analysis yields unique clusters for both temporal and spatial aspects, highlighting the intricate relationship between travel patterns, demographics, and land use. Notably, the study confirms correlations between: morning trips and residential areas, midday trips and commercial/educational areas, and the transactions of marginal traffic zones with near outer residential areas. These insights provide valuable knowledge for policymakers and planners in optimizing land-use and transportation strategies.

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