Collective Human Mobility Patterns: A Case Study Using Data Usage Detail Records

Abstract

Human mobility patterns have been widely investigated due to the application in various fields, such as urban planning and epidemiology. However, most existing researches mainly focus on human mobility patterns at the individual level. In this paper, we analyze collective mobility patterns using data usage detail records (UDRs) for over one million users from the cellular networks in a city of China. We first construct spatial networks where nodes represent base stations (BSs) and edge weights denote users' moving strength between BSs. The difference between communities detected from the networks are then identified. Finally, we discover users with different mobility patterns according to the community difference. Experimental results show that our method can effectively discover users with different mobility patterns. Moreover, we analyze different collective mobility patterns between the Spring Festival vacation and workdays combined with geographical information. Results show that human collective mobility are not always constrained by administrative boundaries, but also affected by social and environmental factors, such as transportations, geographical barriers and area economic types, etc. Besides, the discovered collective mobility patterns can be used to reveal spatial relationship, providing a new insight of human activity pattern and spatial interaction analysis.