A Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together

Giuliano Cornacchia,Luca Pappalardo

doi:10.3390/ijgi10090599

Abstract

Modelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.

Highlights

Human mobility affects crucial aspects of people lives such as the spreading of viral diseases [1,2,3,4,5], public transportation and traffic volumes [4,6,7], urban population and migration [8,9,10], air pollution [11,12], and well-being [13,14]
A solution to deal with geo-privacy issues consists of design generative models of individual mobility, i.e., algorithms able to generate a collection of synthetic trajectories that are realistic in reproducing fundamental human mobility patterns [22,23,24,25,26]
We evaluate the realism of synthetic trajectories generated by the mentioned models in terms of their statistical similarity with real ones extracted from Foursquare checkins [43] (Figure 2)

Summary

Introduction

Human mobility affects crucial aspects of people lives such as the spreading of viral diseases (e.g., the COVID-19 pandemic) [1,2,3,4,5], public transportation and traffic volumes [4,6,7], urban population and migration [8,9,10], air pollution [11,12], and well-being [13,14]. Human mobility plays a fundamental role in the COVID-19 pandemic, as human movements may accelerate the diffusion forcing governments to impose travel restrictions, bans of public gatherings, closures of non-essential businesses, and transitions to homeworking [15]. Even when personal identifiers are removed to anonymize the dataset, there is no guarantee about the protection of the geo-privacy of individuals because they can be re-identified with a small amount of information [16,17,18,19,20,21]. A solution to deal with geo-privacy issues consists of design generative models of individual mobility, i.e., algorithms able to generate a collection of synthetic trajectories that are realistic in reproducing fundamental human mobility patterns [22,23,24,25,26]. While disclosing real data requires a hard-to-control trade-off between uncertainty and utility, synthetic trajectories that preserve statistical properties may achieve in multiple tasks performance comparable to real data

Methods

Results

Discussion

Conclusion