Human mobility models reveal the underlying mechanism of seasonal movements across China

Bing Song, Xiao-Yong Yan, Suoyi Tan, Bin Sai, Shengjie Lai, Hongjie Yu, Chaomin Ou and Xin Lu
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Bing Song: College of Systems Engineering, National University of Defense Technology, Changsha 410073, P. R. China
Xiao-Yong Yan: Institute of Transportation Systems Science and Engineering, Beijing Jiaotong University, Beijing 100091, P. R. China
Suoyi Tan: College of Systems Engineering, National University of Defense Technology, Changsha 410073, P. R. China
Bin Sai: College of Systems Engineering, National University of Defense Technology, Changsha 410073, P. R. China
Shengjie Lai: WorldPop, Department of Geography and Environment, University of Southampton, University Road, Southampton SO171BJ, UK
Hongjie Yu: Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, P. R. China
Chaomin Ou: College of Systems Engineering, National University of Defense Technology, Changsha 410073, P. R. China
Xin Lu: College of Systems Engineering, National University of Defense Technology, Changsha 410073, P. R. China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 04, 1-18

Abstract: Understanding the spatial interactions of human mobility is crucial for urban planning, traffic engineering, as well as for the prevention and control of infectious diseases. Although many models have been developed to model human mobility, it is not clear whether such models could also capture the traveling mechanisms across different time periods (e.g. workdays, weekends or holidays). With one-year long nationwide location-based service (LBS) data in China, we investigate the spatiotemporal characteristics of population movements during different time periods, and make thorough comparisons for the applicability of five state-of-the-art human mobility models. We find that population flows show significant periodicity and strong inequality across temporal and spatial distribution. A strong “backflow†effect is found for cross-city movements before and after holidays. Parameter fitting of gravity models reveals that travels in different type of days consider the attractiveness of destinations and cost of distance differently. Surprisingly, the comparison indicates that the parameter-free opportunity priority selection (OPS) model outperforms other models and is the best to characterize human mobility in China across all six different types of days. However, there is still an urgent need for development of more dedicated models for human mobility on weekends and different types of holidays.

Keywords: Population movement; seasonal migration; human mobility; human mobility models (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0129183122500541

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