Urban-Scale Human Mobility Modeling with Multi-Source Urban Network Data

Desheng Zhang, Tian He, Fan Zhang, Chengzhong Xu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Expanding our knowledge about human mobility is essential for building efficient wireless protocols and mobile applications. Previous mobility studies have typically been built upon empirical single-source data (e.g., cellphone or transit data), which inevitably introduces a bias against residents not contributing this type of data, e.g., call detail records cannot be obtained from the residents without cellphone activities, and transit data cannot cover the residents who walk or ride private vehicles. To address this issue, we propose and implement a novel architecture mPat to explore human mobility using multi-source urban network data. A reference implementation of mPat was developed at an unprecedented scale upon the urban infrastructures of Shenzhen, China. The novelty and uniqueness of mPat lie in its three layers: 1) a data feed layer consisting of real-time data feeds from various urban networks with 24 thousand vehicles, 16 million smart cards, and 10 million cellphones; 2) a mobility abstraction layer exploring correlation and divergence among multi-source data to infer human mobility with a context-aware optimization model based on block coordinate decent; and 3) an application layer to improve urban efficiency based on the human mobility findings of the study. The evaluation shows that mPat achieves a 79% inference accuracy, and that its real-world application reduces passenger travel time by 36%.

Original languageEnglish (US)
Pages (from-to)671-684
Number of pages14
JournalIEEE/ACM Transactions on Networking
Issue number2
StatePublished - Apr 2018

All Science Journal Classification (ASJC) codes

  • Software
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Computer Science Applications


  • Urban networks
  • human mobility
  • network modeling
  • smart cities


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