A Review of Urban Population Mobility Perception and Modeling Methods

In recent years, the surge of geospatial data related to population mobility has created unprecedented opportunities for quantitatively studying population mobility patterns. This influx of data has led to the development of numerous methods and models that capture and reproduce the spatiotemporal structures and dynamics of population mobility, providing a scientific basis for urban planning, transportation, public health, and other urban applications. Consequently, it has significantly enhanced our understanding and management of urban environments. From the perspective of the intersection between human mobility research and spatial interaction studies, we review the research framework and recent advancements in urban population mobility perception and modeling methods. We further discuss their applications in urban development and anticipate future research directions, aiming to inspire and facilitate innovation and deeper application in population mobility research. (1) Types and Processing Methods of Urban Population Mobility Data: This section analyzes various kinds of urban population mobility data, including location point data, trajectory data, and flow data, while addressing issues related to data reliability analysis and privacy protection. (2) Perceiving Methods for Urban Population Mobility: This section introduces methods for understanding population mobility dynamics, network analysis, and population mobility data mining, which provide deeper insights into the spatiotemporal interaction patterns of population mobility. (3) Modeling Methods for Urban Population Mobility: This part provides a detailed exposition of modeling methods, encompassing both mechanism-driven and data-driven models, such as gravity models, intervening opportunity models, population flow prediction models, and origin-destination flow generation models. These methods provide the theoretical foundation and tools for describing, simulating, and predicting urban population mobility. (4) Applications of Population Mobility Perception and Modeling: This section explores the application of research in urban planning, transportation, public health, and public safety, illustrating how these findings can provide scientific support for addressing urban issues. For example, in urban planning, population mobility data can aid in identifying urban spatial structures and regional functions; in transportation management, flow models can predict traffic volumes and optimize traffic planning; in public health, mobility models can simulate and control the spread of infectious diseases; and in public safety, mobility perception technologies benefit disaster early warning and emergency response.
Despite significant advancements in population mobility perception and modeling, existing research still faces shortcomings, such as low spatiotemporal accuracy, poor model interpretability, and insufficient understanding of urban application scenarios. Based on an analysis of these issues, we propose potential research directions for the future, including the acquisition of high spatiotemporal resolution population mobility data through multi-source data integration, the construction of hierarchical population mobility networks, mechanism-driven population mobility modeling, and scenario-driven urban applications. These research directions are hopeful to significantly advance the development of population mobility perception and modeling, providing powerful scientific support for urban applications. As the pace of urbanization accelerates and information technology evolves, the study of population mobility perception and modeling will play an increasingly important role in urban management and services.