Exploring the spatial-temporal arrangements of urban activity space from individual's daily commute: A Geospatial-Agent based Approach Using Empirical Data

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Abstract

Abstract The study explores the significance of individual mobility measures, such as activity space, in understanding how individuals interact with their daily environments. Existing measures often overlook geographical concepts like spatial-temporal arrangements of activity spaces, focusing solely on numerical assessments. To address this gap, a multi-level modeling approach combining Agent-Based Modeling (ABM) and Geographic Information Systems (GIS) is utilized to simulate activity destination selection throughout a workday in Zanjan, Iran. The model integrates individual preferences, built environment characteristics, network attributes, and travel generation data. Real-world data from Emerging Data Sources (EDSs) validate the model's reliability and accuracy. Key findings include: (1) clustering analysis identifying four types of activity destinations at different hourly intervals, (2) a central activity space acting as a hub for activity-based travel with a monocentric distribution pattern, (3) individual preference for destinations with diverse and dense built environments, and (4) a decrease in trip frequency as distance from the main activity space increases, indicating a spatial decay effect on activity-based travels.