Non-Markovian Character in Human Mobility: Online and Offline

TL;DR

This paper reveals the non-Markovian nature of human mobility in online and offline contexts, demonstrating its impact on predictability and proposing a model that better captures observed behaviors.

Contribution

It introduces a new model incorporating preferential return, inertial effect, and exploration to better replicate non-Markovian human mobility patterns.

Findings

Non-Markovian character observed in both online and offline human mobility.

Lower entropy and higher predictability linked to non-Markovian dynamics.

Proposed model closely reproduces empirical mobility characteristics.

Abstract

The dynamics of human mobility characterizes the trajectories humans follow during their daily activities and is the foundation of processes from epidemic spreading to traffic prediction and information recommendation. In this paper, we investigate a massive data set of human activity including both online behavior of browsing websites and offline one of visiting towers based mobile terminations. The non-Markovian character observed from both online and offline cases is suggested by the scaling law in the distribution of dwelling time at individual and collective levels, respectively. Furthermore, we argue that the lower entropy and higher predictability in human mobility for both online and offline cases may origin from this non-Markovian character. However, the distributions of individual entropy and predictability show the different degrees of non-Markovian character from online to offline cases. To accounting for non-Markovian character in human mobility, we introduce a protype model with three basic ingredients, \emph{preferential return, inertial effect, and exploration} to reproduce the dynamic process of online and offline human mobility. In comparison with standard and biased random walk models with assumption of Markov process, the proposed model is able to obtain characters much closer to these empirical observations.