Representing Urban Functions through Zone Embedding with Human Mobility Patterns
Representing Urban Functions through Zone Embedding with Human Mobility Patterns
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DOI：
10.24963/ijcai.2018/545
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Zijun Yao;Yanjie Fu;Bin Liu-;Wangsu Hu;Hui Xiong
中科院分区：
文献类型：
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作者：
Zijun Yao;Yanjie Fu;Bin Liu-;Wangsu Hu;Hui Xiong
城市功能是指城市各区域各司其职、相互配合、服务于人民群众不同生活需求的土地利用目的。了解区域功能有助于解决各种与城市相关的问题，例如增加交通容量和增强基于位置的服务。因此，研究如何学习城市区域在城市功能方面的表征，有利于更好地支持城市分析应用。为此，在本文中，我们提出了一个框架，通过利用大规模出租车轨迹来学习城市区域的向量表示（嵌入）。具体来说，我们从出租车轨迹中提取人员流动模式，并利用起点-目的地区域的共现来学习区域嵌入。为了利用人类移动模式的时空特征，我们将移动方向、出发/到达时间、目的地景点和旅行距离纳入区域嵌入的建模中。我们对纽约市的真实城市数据集进行了广泛的实验。实验结果证明了所提出的嵌入模型用人员流动数据来表示区域城市功能的有效性。
Urban functions refer to the purposes of land use in cities where each zone plays a distinct role and cooperates with each other to serve people’s various life needs. Understanding zone functions helps to solve a variety of urban related problems, such as increasing traffic capacity and enhancing location-based service. Therefore, it is beneficial to investigate how to learn the representations of city zones in terms of urban functions, for better supporting urban analytic applications. To this end, in this paper, we propose a framework to learn the vector representation (embedding) of city zones by exploiting large-scale taxi trajectories. Specifically, we extract human mobility patterns from taxi trajectories, and use the co-occurrence of origin-destination zones to learn zone embeddings. To utilize the spatio-temporal characteristics of human mobility patterns, we incorporate mobility direction, departure/arrival time, destination attraction, and travel distance into the modeling of zone embeddings. We conduct extensive experiments with real-world urban datasets of New York City. Experimental results demonstrate the effectiveness of the proposed embedding model to represent urban functions of zones with human mobility data.