A comprehensive methodological review of human mobility simulation and modelling: Current trends, challenges, and future directions

作者：Zhihua Zhong, H. W. Zhang, Jun’ichi Ozaki, Yang Zhou, Xinjie Zhao, Daniel Dan, Chaofan Wang · 发表于：Physica A Statistical Mechanics and its Applications · 年份：2025 · DOI：10.1016/j.physa.2025.130791 · 被引用次数：8 · 研究领域：Human Mobility and Location-Based Analysis、Urban Transport and Accessibility、Transportation and Mobility Innovations

Human mobility, reflecting the behaviour and movement patterns of individuals or groups in space, presents intricate characteristics and impacts various dimensions of urban life. Having increasingly caught the attention of disciplinary scholars, this field has evolved into a confused mixture of various modelling theories and methodologies, creating challenges in selecting appropriate methods when dealing with data with different structures and applications with varying scales of observation and scenarios. Moreover, disruptive techniques such as big data and artificial intelligence have tremendously revolutionised the traditional research paradigms in human mobility simulation and modelling. To scrutinise the various emerging methods, this study comprehensively reviews state-of-the-art research in the field, particularly focusing on research over the past decades. Here, we holistically collect, classify, and summarise existing methodologies into two categories: data-driven vs. mechanism-driven. These methods are organised following key clues, including modelling focus (aggregated flow vs. individual trajectory), typical application scenarios (regular vs. irregular), and model complexity (simple vs. complex), and are presented chronologically. Notably, deep learning (DL), agent-based model (ABM), and their combinations are emphasised as the most cutting-edge directions. We also reveal the future trends and opportunities for model evolution, transitioning from single-model, sing...