Sezaki & Nishiyama Laboratory
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Hidenaga Ushijima, Shunsuke Aoki, Peng Helinyi, Yuuki Nishiyama, Kaoru Sezaki
An Unsupervised Learning-based Approach for User Mobility Analysis of E-Scooter Sharing Systems Inproceedings
In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), IEEE, 2021.
BibTeX | タグ: data analysis, e-scooter | Links:
@inproceedings{itsc2021_ntf,
title = {An Unsupervised Learning-based Approach for User Mobility Analysis of E-Scooter Sharing Systems},
author = {Hidenaga Ushijima and Shunsuke Aoki and Peng Helinyi and Yuuki Nishiyama and Kaoru Sezaki
},
url = {https://2021.ieee-itsc.org/},
doi = {10.1109/ITSC48978.2021.9564616},
year = {2021},
date = {2021-09-19},
booktitle = {2021 IEEE International Intelligent Transportation Systems Conference (ITSC)},
journal = {2021 IEEE International Conference on Intelligent Transportation - ITSC},
publisher = {IEEE},
keywords = {data analysis, e-scooter},
pubstate = {published},
tppubtype = {inproceedings}
}
Kyoichi Ito, Masaki Ito, Kosuke Miyazaki, Keishi Tanimoto, Kaoru Sezaki
Data analysis on train transportation data with nonnegative matrix factorization Inproceedings
In: 2017 IEEE International Conference on Big Data (Big Data), 11-14 Dec. 2017, Boston, MA, USA, pp. 4080–4085, ieeexplore.ieee.org, 2018.
Abstract | BibTeX | タグ: data analysis, NMF, Smart cards, Transportation | Links:
@inproceedings{Ito2017-ey,
title = {Data analysis on train transportation data with nonnegative matrix factorization},
author = {Kyoichi Ito and Masaki Ito and Kosuke Miyazaki and Keishi Tanimoto and Kaoru Sezaki},
doi = {10.1109/BigData.2017.8258425},
year = {2018},
date = {2018-01-15},
booktitle = {2017 IEEE International Conference on Big Data (Big Data), 11-14 Dec. 2017, Boston, MA, USA},
pages = {4080--4085},
publisher = {ieeexplore.ieee.org},
abstract = {In light of the recognized need to collect and analyze data to maintain urban development, the “smart city” concept has gained much attention recently. The development of sensing and information techniques has facilitated the analysis of urban mobility to better understand the characteristics of cities. Of the information and data that can be used to characterize cities, transportation data are among the most useful because transportation is so closely related to human and other aspects of urban mobility. In extracting features from automatically collected data, the greatest difficulty comes from the size or complexity of the data set, as these often have too many attributes or indices to analyze. This paper discusses the results of analyses of smart card ticketing authentication logs using nonnegative matrix factorization (NMF). The results present extracted features applicable to assessing various user and station characteristics and dynamics.},
keywords = {data analysis, NMF, Smart cards, Transportation},
pubstate = {published},
tppubtype = {inproceedings}
}