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.