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

2 Scopus citations

Abstract

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
Volume26
Issue number2
DOIs
StatePublished - Apr 2018

Bibliographical note

Funding Information:
Manuscript received January 8, 2017; revised July 1, 2017; accepted August 16, 2017; approved by IEEE/ACM TRANSACTIONS ON NETWORK-ING Editor J. Xie. Date of publication March 19, 2018; date of current version April 16, 2018. This work was supported in part by the U.S. NSF under Grant CNS-1544887 and Grant CNS-1446640 and in part by the China 973 Program under 2015CB352400. A preliminary result of this work was published at [13]. (Corresponding author: Desheng Zhang.) D. Zhang is with the Department of Computer Science, Rutgers University, New Brunswick, NJ 08901 USA (e-mail: d.z@rutgers.edu).

Keywords

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

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