Representing Urban Functions through Zone Embedding with Human Mobility Patterns

Zijun Yao, Yanjie Fu, Bin Liu, Wangsu Hu, Hui Xiong
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
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
more » ... 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.
doi:10.24963/ijcai.2018/545 dblp:conf/ijcai/YaoFLHX18 fatcat:r5nk2yhouvdtjajzwbcmujyabi