Representing Urban Functions through Zone Embedding with Human Mobility Patterns
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.
Z. Yao et al., "Representing Urban Functions through Zone Embedding with Human Mobility Patterns," Proceedings of the 27th International Joint Conference on Artificial Intelligence (2018, Stockholm, Sweden), pp. 3919-3925, International Joint Conferences on Artificial Intelligence, Jul 2018.
The definitive version is available at https://doi.org/10.24963/ijcai.2018/545
27th International Joint Conference on Artificial Intelligence, IJCAI-18 (2018: July 13-19, Stokholm, Sweden)
Intelligent Systems Center
Keywords and Phrases
Land use; Taxicabs; Human mobility; New York city; Origin destination; Spatiotemporal characteristics; Traffic capacity; Travel distance; Vector representations; Zone function; Artificial intelligence
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Article - Conference proceedings
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01 Jul 2018