The Eigenmode Analysis of Human Motion

TL;DR

This paper applies eigenmode analysis to large-scale human motion data from communication records to understand the scaling properties and characteristics of human movement, aiding urban planning and theoretical insights.

Contribution

It introduces a novel eigenmode analysis framework for human motion data, providing new insights into the scaling and characteristics of human mobility.

Findings

Eigenmode analysis reveals scaling properties of human motion

Characterizes the spatial and temporal patterns of human movement

Provides a theoretical basis for modeling human mobility

Abstract

Rapid advances in modern communication technology are enabling the accumulation of large-scale, high-resolution observational data of spatiotemporal movements of humans. Classification and prediction of human mobility based on the analysis of such data carry great potential in applications such as urban planning as well as being of theoretical interest. A robust theoretical framework is therefore required to study and properly understand human motion. Here we perform the eigenmode analysis of human motion data gathered from mobile communication records, which allows us to explore the scaling properties and characteristics of human motion.