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Population mobility modelling for mobility data simulation
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DOI：10.1016/j.compenvurbsys.2020.101526.png)
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Abstract
En 中文
Mobility models have a broad range of applications in areas related to human movements, such as urban planning, transportation, and simulations of diseases spread. In the last decade, the extensive geolocated user trajectories collected from mobile devices allowed for more realistic mobility modelling, improving its accuracy. However, mobility data sharing raises privacy concerns, which in turn limits accessibility to the data. In this paper, we propose a WHO-WHERE-WHEN (3W) model, an improved privacy-protective mobility modelling method for synthetic mobility data generation. Based on real trajectories, it produces artificial user mobility trajectories that simulate population fluctuations in a study area, and thus preserves the individual's privacy. The model simulates the individual spatiotemporal aspects of lives accurately, representing real population flows and distributions. The proposed method was inspired by the Work and Home Extracted REgions (WHERE) algorithm, but we have extended it by considering the activity space and circadian rhythm of people. Furthermore, we propose a clustering approach to capture and reproduce the heterogeneous characteristic of mobility. We evaluate our model and compare its performance to the WHERE algorithm on the synthetic and real data test cases. Use of the 3W model improved the accuracy of population distribution reproduction by 35% measured using Earth Mover's Distance. The travel distances and the spatial distribution of the flows reproduced by the 3W model match input data with high accuracy. We also evaluate the level of privacy protection by comparing synthesised and input datasets. We find that no daily trajectory can be matched between input and synthesised datasets and the average length of the matching sequence of visited locations to contain only two locations.
Keywords:
Human mobility modelling
Privacy preservation
Movement trajectories
Mobility data
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