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Modeling urban scale human mobility through big data analysis and machine learning

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Abstract

In the United States, the buildings sector consumes about 76% of electricity use and 40% of all primary energy use and associated greenhouse gas emissions. Occupant behavior has drawn increasing research interests due to its impacts on the building energy consumption. However, occupant behavior study at urban scale remains a challenge, and very limited studies have been conducted. As an effort to couple big data analysis with human mobility modeling, this study has explored urban scale human mobility utilizing three months Global Positioning System (GPS) data of 93,000 users at Phoenix Metropolitan Area. This research extracted stay points from raw data, and identified users’ home, work, and other locations by Density-Based Spatial Clustering algorithm. Then, daily mobility patterns were constructed using different types of locations. We propose a novel approach to predict urban scale daily human mobility patterns with 12-hour prediction horizon, using Long Short-Term Memory (LSTM) neural network model. Results shows the developed models achieved around 85% average accuracy and about 86% mean precision. The developed models can be further applied to analyze urban scale occupant behavior, building energy demand and flexibility, and contributed to urban planning.

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Acknowledgements

This work was supported by the U.S. National Science Foundation (Award No. 1949372 and No. 2125775); and in part supported through computational resources provided by Syracuse University.

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Authors and Affiliations

  1. Department of Mechanical & Aerospace Engineering, Syracuse University, 263 Link Hall, Syracuse, NY, 13244, USA

    Yapan Liu & Bing Dong

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  1. Yapan Liu
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  2. Bing Dong
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Contributions

Bing Dong: conceptualization, methodology, investigation, resources, writing—review, funding acquisition, supervision. Yapan Liu: conceptualization, methodology, investigation, formal analysis, writing—original draft, writing—review & editing, visualization.

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Correspondence to Bing Dong.

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The authors have no competing interests to declare that are relevant to the content of this article. Bing Dong is an Associate Editor of Building Simulation.

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This study does not contain any studies with human or animal subjects performed by any of the authors.

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Liu, Y., Dong, B. Modeling urban scale human mobility through big data analysis and machine learning. Build. Simul. 17, 3–21 (2024). https://doi.org/10.1007/s12273-023-1043-z

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