Title

Representing Urban Functions through Zone Embedding with Human Mobility Patterns

Abstract

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.

Meeting Name

27th International Joint Conference on Artificial Intelligence, IJCAI-18 (2018: July 13-19, Stokholm, Sweden)

Department(s)

Computer Science

Comments

This research was partially supported by IFLYTEK Co., Ltd. and the National Science Foundation (NSF) via the grant number IIS-1648664.

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

International Standard Book Number (ISBN)

978-0-9992411-2-7

International Standard Serial Number (ISSN)

1045-0823

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 International Joint Conferences on Artificial Intelligence, All rights reserved.

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