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.
Highlights
As location-based sensor devices and networks have been widely spread, a large amount of mobility data of users, which can be potentially used for several research purposes, has been accumulated [1] [2]
Our proposed models can contribute to the prediction of people flows by discovering underlying representations of geospatial areas from mobility data
If we regard massive transition patterns of people on an area as the context of its area, we can notice that the characteristics or roles of the area are dynamically changing according to its context of how people move on the area and for what purpose people visit the area
Summary
As location-based sensor devices and networks have been widely spread, a large amount of mobility data of users, which can be potentially used for several research purposes, has been accumulated [1] [2] In addition to such sensor devices, the deployment of recent infrastructure for public transit such as automated fare collection (AFC) systems with smart cards has supported the collection of large volumes of mobility data including people’s activities with detailed time and space information [3]. Researchers have used such large amount of mobility data for the purpose of location-based recommendation such as personalization point of interest (POI) [4]. If we can obtain such latent representation of areas, it contributes to modeling and predicting people flow with massive mobility data more effectively and precisely
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