Heterogeneous model integration for multi-source urban infrastructure data

Desheng Zhang, Juanjuan Zhao, Fan Zhang, Tian He, Haengju Lee, Sang H. Son

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Data-driven modeling usually suffers from data sparsity, especially for large-scale modeling for urban phenomena based on single-source urban-infrastructure data under fine-grained spatial-temporal contexts. To address this challenge, we motivate, design, and implement UrbanCPS, a cyber-physical system with heterogeneous model integration, based on extremely-large multi-source infrastructures in the Chinese city Shenzhen, involving 42,000 vehicles, 10 million residents, and 16 million smartcards. Based on temporal, spatial, and contextual contexts, we formulate an optimization problem about how to optimally integrate models based on highly diverse datasets under three practical issues, that is, heterogeneity of models, input data sparsity, or unknown ground truth. We further propose a real-world application called Speedometer, inferring real-time traffic speeds in urban areas. The evaluation results show that, compared to a state-ofthe- art system, Speedometer increases the inference accuracy by 29% on average.

Original languageEnglish (US)
Article number4
JournalACM Transactions on Cyber-Physical Systems
Issue number1
StatePublished - 2017

Bibliographical note

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© 2016 ACM.


  • Cyber-physical system
  • Model integration


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