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

Zhihua Zhong, Hongzeng Zhang, Ozaki, Jun’ichi, Yang Zhou, Xinjie Zhao, Daniel Dan and Chaofan Wang

Physica A: Statistical Mechanics and its Applications, 2025, vol. 674, issue C

Abstract: 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, single-modality, and single-agent to multi-model, multi-modality, and multi-agent systems. Meanwhile, challenges in data ethics and bias, and models’ scalability, predictability, interpretability, and verifiability should be addressed in the future. The discoveries will serve as a reference for scholars and practitioners in the field, contributing to a systematic methodological framework that clarifies the complex research landscape and establishes a baseline for future model development.

Keywords: Human mobility simulation and modelling; Transport system; Deep learning; Agent-based model (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:674:y:2025:i:c:s0378437125004431

DOI: 10.1016/j.physa.2025.130791

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