首页|Modeling urban scale human mobility through big data analysis and machine learning
Modeling urban scale human mobility through big data analysis and machine learning
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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.
urban human mobilitybig data analysisurban scale occupant behaviorrecurrent neural networks
Yapan Liu、Bing Dong
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Department of Mechanical & Aerospace Engineering,Syracuse University,263 Link Hall,Syracuse,NY 13244,USA
U.S.National Science FoundationU.S.National Science Foundationcomputational resources provided by Syracuse University
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