Mobile Phone Data in Urban Commuting: A Network Community Detection-Based Framework to Unveil the Spatial Structure of Commuting Demand

Abstract

As the outcomes of rapid urbanization, the spatial separation of homes and workplaces extends the commuting distance and complicates the commuting demand of residents. To promote urban livability and sustainability, it becomes crucially important to understand the commuting patterns by decomposing and simplifying the diverse commuting demand. In this paper, a methodology framework is proposed to describe the spatial structure of commuting demand in a city using mobile phone data. Four steps are mainly included in the proposed methodology: the preprocessing of mobile phone data, the labeling of individuals and their activity points, the construction of the jobs-housing relationship network, and the network decomposition based on the community detection algorithm. To demonstrate the practical use of the proposed methodologies, a case study is carried out in Shanghai to explore the commuting patterns of Shanghai residents. The result indicates the regions with dense jobs-housing connections and cross-regional commuting demand. The result also finds that the administrative boundaries show a significant effect on the residential commuting behavior and the metro lines on the cross-regional commuting behavior. The results generated by the methodology proposed can be referenced by policymakers to support urban transportation planning and promote urban livability and sustainability.