Article citationsMore>>

Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S. and Dean, J. (2013) Distributed Representations of Words and Phrases and Their Compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z. and Weinberger, K.Q., Eds., Advances in Neural Information Processing Systems 26, Curran Associates, Inc., 3111-3119.

has been cited by the following article:

• TITLE: Geospatial Area Embedding Based on the Movement Purpose Hypothesis Using Large-Scale Mobility Data from Smart Card

  AUTHORS: Masanao Ochi, Yuko Nakashio, Matthew Ruttley, Junichiro Mori, Ichiro Sakata

  KEYWORDS: Network Embedding, Auto Fare Collection, Geographic Information System, Trajectory Data Mining, Spatial Databases

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.9 No.11, November 24, 2016

  ABSTRACT: With the deployment of modern infrastructure for public transportation, several studies have analyzed movement patterns of people using smart card data and have characterized different areas. In this paper, we propose the “movement purpose hypothesis” that each movement occurs from two causes: where the person is and what the person wants to do at a given moment. We formulate this hypothesis to a synthesis model in which two network graphs generate a movement network graph. Then we develop two novel-embedding models to assess the hypothesis, and demonstrate that the models obtain a vector representation of a geospatial area using movement patterns of people from large-scale smart card data. We conducted an experiment using smart card data for a large network of railroads in the Kansai region of Japan. We obtained a vector representation of each railroad station and each purpose using the developed embedding models. Results show that network embedding methods are suitable for a large-scale movement of data, and the developed models perform better than existing embedding methods in the task of multi-label classification for train stations on the purpose of use data set. Our proposed models can contribute to the prediction of people flows by discovering underlying representations of geospatial areas from mobility data.