Modeling urban scale human mobility through big data analysis and machine learning
Modeling urban scale human mobility through big data analysis and machine learning
复制标题
通过大数据分析和机器学习来模拟城市规模的人员流动
DOI：
10.1007/s12273-023-1043-z
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发表时间：
2023-08
影响因子：
5.5
通讯作者：
Yapan Liu;B. Dong
中科院分区：
文献类型：
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作者：
Yapan Liu;B. Dong
在美国，建筑部门消耗了约76%的电力和40%的一次能源消耗和相关的温室气体排放。居民行为因其对建筑能耗的影响而引起越来越多的研究兴趣。然而，城市尺度下的居住者行为研究仍然是一个挑战，而且进行的研究非常有限。为了将大数据分析与人员流动性建模结合起来，这项研究利用全球定位系统(GPS)三个月来对凤凰城大都市区93,000名用户的数据进行了城市规模的人员流动性研究。本研究从原始数据中提取停留点，并通过基于密度的空间聚类算法识别用户的家、单位等位置。然后，使用不同类型的位置构建了日常流动模式。提出了一种新的基于长短期记忆(LSTM)神经网络模型的12小时预测范围内的城市尺度每日人员流动模式预测方法。结果表明，所开发的模型达到了85%左右的平均精度和86%左右的平均精度。所建立的模型可进一步用于分析城市规模居民行为、建筑能源需求和灵活性，为城市规划做出贡献。
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.