The Effect of Recency to Human Mobility

TL;DR

This paper introduces a new human mobility model emphasizing recent visits, demonstrating it better explains movement patterns across various datasets, with implications for urban planning and epidemic modeling.

Contribution

The study proposes a recency-based human mobility model that improves upon existing models by incorporating recent visits, enhancing the understanding of human movement dynamics.

Findings

Recency-based model outperforms traditional frequency-based models in explaining human trajectories.

Empirical data shows recent visits significantly influence movement patterns.

Model improves predictions of human mobility in diverse datasets.

Abstract

In recent years, we have seen scientists attempt to model and explain human dynamics and, in particular, human movement. Many aspects of our complex life are affected by human movements such as disease spread and epidemics modeling, city planning, wireless network development, and disaster relief, to name a few. Given the myriad of applications it is clear that a complete understanding of how people move in space can lead to huge benefits to our society. In most of the recent works, scientists have focused on the idea that people movements are biased towards frequently-visited locations. According to them, human movement is based on an exploration/exploitation dichotomy in which individuals choose new locations (exploration) or return to frequently-visited locations (exploitation). In this work, we focus on the concept of recency. We propose a model in which exploitation in human movement also considers recently-visited locations and not solely frequently-visited locations. We test our hypothesis against different empirical data of human mobility and show that our proposed model is able to better explain the human trajectories in these datasets.