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Radfar, Shariat, Koosha, Hamidreza, Gholami, Ali, Amindoust, Atefeh. (1404). Unveiling Spatiotemporal Patterns in Public Transportation: A Smart Card Data Analysis. , (), -. doi: 10.22059/ceij.2025.385897.2189
Shariat Radfar; Hamidreza Koosha; Ali Gholami; Atefeh Amindoust. "Unveiling Spatiotemporal Patterns in Public Transportation: A Smart Card Data Analysis". , , , 1404, -. doi: 10.22059/ceij.2025.385897.2189
Radfar, Shariat, Koosha, Hamidreza, Gholami, Ali, Amindoust, Atefeh. (1404). 'Unveiling Spatiotemporal Patterns in Public Transportation: A Smart Card Data Analysis', , (), pp. -. doi: 10.22059/ceij.2025.385897.2189
Radfar, Shariat, Koosha, Hamidreza, Gholami, Ali, Amindoust, Atefeh. Unveiling Spatiotemporal Patterns in Public Transportation: A Smart Card Data Analysis. , 1404; (): -. doi: 10.22059/ceij.2025.385897.2189

Unveiling Spatiotemporal Patterns in Public Transportation: A Smart Card Data Analysis

Civil Engineering Infrastructures Journal
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 19 مهر 1404    اصل مقاله (1.56 M)
شناسه دیجیتال (DOI): 10.22059/ceij.2025.385897.2189
نویسندگان
Shariat Radfar1؛ Hamidreza Koosha2؛ Ali Gholami* 3؛ Atefeh Amindoust1
1Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Iran
3Department of Civil Engineering, Faculty of Engineering, Golestan University, Gorgan, Iran
چکیده
Abstract
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.
کلیدواژه‌ها
Smart Card Data (SCD)؛ Temporal and Spatial Patterns؛ Clustering of Traffic Zones؛ K-Means Algorithm؛ Mean Shift Algorithm
آمار
تعداد مشاهده مقاله: 251
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