extended-abstract

Representation learning for geospatial areas using large-scale mobility data from smart card

Authors:

Yuta Yamashita,

Kimitake Asatani,

Matthew Ruttley,

Junichiro MoriAuthors Info & Claims

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

Pages 1381 - 1389

https://doi.org/10.1145/2968219.2968416

Published: 12 September 2016 Publication History

Abstract

With the deployment of modern infrastructures for public transit, several studies have analyzed the transition patterns of people by using smart card data and have characterized the areas. In this paper, we propose a novel embedding method to obtain a vector representation of a geospatial area using transition patterns of people from the large-scale data of their smart cards. We extend a network embedding by taking into account geographical constraints on people transitioning in the real world. We conducted an experiment using smart card data in a large network of railroads in Kansai areas in Japan. We obtained a vector representation of each railroad station using the proposed embedding method. The results show that the proposed method performs better than the existing network embedding methods in the task of multi-label classification for purposes of going to a railroad station. Our proposed method can contribute to predicting people flow by discovering underlying representations of geospatial areas from mobility data.

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Cited By

Li YZhang TLv XLu YWang W(2023)Profiling Public Transit Passenger Mobility Using Adversarial LearningISPRS International Journal of Geo-Information10.3390/ijgi1208033812:8(338)Online publication date: 12-Aug-2023
https://doi.org/10.3390/ijgi12080338
Gramacki PLeśniara KRaczycki KWoźniak SPrzymus MSzymański P(2023)SRAIProceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3615886.3627740(43-52)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615886.3627740
Amara ATaieb MAouicha M(2021)Network representation learning systematic review: Ancestors and current development stateMachine Learning with Applications10.1016/j.mlwa.2021.100130(100130)Online publication date: Aug-2021
https://doi.org/10.1016/j.mlwa.2021.100130
Show More Cited By

Index Terms

Representation learning for geospatial areas using large-scale mobility data from smart card
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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Published In

cover image ACM Conferences

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

September 2016

1807 pages

ISBN:9781450344623

DOI:10.1145/2968219

General Chairs:
Paul Lukowicz
DFKI and University of Kaiserslautern
,
Antonio Krüger
DFKI and Saarland University
,
Program Chairs:
Andreas Bulling
Max Planck Institute for Informatics
,
Youn-Kyung Lim
KAIST
,
Shwetak N. Patel
University of Washington

Copyright © 2016 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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New York, NY, United States

Publication History

Published: 12 September 2016

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UbiComp '16

Sponsor:

UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 12 - 16, 2016

Germany, Heidelberg

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Li YZhang TLv XLu YWang W(2023)Profiling Public Transit Passenger Mobility Using Adversarial LearningISPRS International Journal of Geo-Information10.3390/ijgi1208033812:8(338)Online publication date: 12-Aug-2023
https://doi.org/10.3390/ijgi12080338
Gramacki PLeśniara KRaczycki KWoźniak SPrzymus MSzymański P(2023)SRAIProceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3615886.3627740(43-52)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615886.3627740
Amara ATaieb MAouicha M(2021)Network representation learning systematic review: Ancestors and current development stateMachine Learning with Applications10.1016/j.mlwa.2021.100130(100130)Online publication date: Aug-2021
https://doi.org/10.1016/j.mlwa.2021.100130
Zhiyuli ALiang XChen YDu X(2019)Modeling Large-Scale Dynamic Social Networks via Node EmbeddingsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.287260231:10(1994-2007)Online publication date: 1-Oct-2019
https://doi.org/10.1109/TKDE.2018.2872602
Cai HZheng VChang K(2018)A Comprehensive Survey of Graph Embedding: Problems, Techniques, and ApplicationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.280745230:9(1616-1637)Online publication date: 1-Sep-2018
https://doi.org/10.1109/TKDE.2018.2807452

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