Journals
Sorry, no publications matched your criteria.
Inproceedings
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 Refereed
In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp. 1425-1430, IEEE, 2021, ISBN: 978-1-7281-9141-6.
@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},
isbn = {978-1-7281-9141-6},
year = {2021},
date = {2021-10-25},
urldate = {2021-10-25},
booktitle = {2021 IEEE International Intelligent Transportation Systems Conference (ITSC)},
pages = {1425-1430},
publisher = {IEEE},
abstract = {Human mobility analysis is a key method for understanding urban dynamics and mobility optimization. Novel last-mile mobility, called micromo-bilities, that includes shared bicycles, electric bicycles (e-bikes), and electric scooters are seeing rapid widespread acceptance in major cities. Compared with existing mobility data such as cars, buses, and trains, the majority trip distance of micromobilities is short, typically less than a few miles. The riders use them for commuting, sightseeing, shopping, and/or fun. By using the mobility data of micromobilities, we can observe more fine-grained human mobility in urban areas than existing data sources. In this paper, we present an unsupervised learning-based technique to analyze human mobility in urban areas and to study user clusters for such micromobility services. In our approach, we cluster user mobility patterns by using non-negative tensor factorization (NTF) from area-based trip data (which only included locations of origin and destination). Our approach was applied to micromobility data collected from Chicago and Washington, D.C., and we observed characteristic patterns.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Zengyi Han, Hong Duc Nguyen, Shunsuke Aoki, Yuuki Nishiyama, Kaoru Sezaki
MiMoSense: An Open Crowdsensing Platform for Micro-Mobility Inproceedings Refereed
In: 2021 IEEE International Conference on Intelligent Transportation (ITSC), pp. 1-6, IEEE, 2021.
@inproceedings{ieee_itsc_mimosense,
title = {MiMoSense: An Open Crowdsensing Platform for Micro-Mobility},
author = {Zengyi Han and Hong Duc Nguyen and Shunsuke Aoki and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://2021.ieee-itsc.org/},
doi = {10.1109/ITSC48978.2021.9564524},
year = {2021},
date = {2021-09-19},
urldate = {2021-09-19},
booktitle = {2021 IEEE International Conference on Intelligent Transportation (ITSC)},
pages = {1-6},
publisher = {IEEE},
abstract = {The use of micro-mobility (e.g., bicycle and scooter) and their data for urban sensing and rider assessment is becoming increasingly popular in research. However, different research topics require different sensor setups; no general data collecting tools for the micro-mobility makes the researcher who wishes to collect data has to build their own collecting system from scratch. To this end, we present MiMoSense, an open crowdsensing platform for micro-mobility. MiMoSense consists of two components: (1) MiMoSense server, which is set up on the cloud, and used to manage sensing studies and the collected data for research and sharing. (2) MiMoSense client, uses micro-mobility carrying various sensors and IoT devices to collect multiple kinds of data during traveling. As a reusable open-source software, MiMoSense shifts the researcher's focus from software development to sensing data analysis; it can help researchers quickly develop an extensible platform for collecting micro-mobility's raw sensing data and inferring traveling context. We have evaluated MiMoSense's battery consumption, message latency and discuss its use.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Hidenaga Ushijima, Shota Ono, Yuuki Nishiyama, Kaoru Sezaki
Towards Infectious Disease Risk Assessment in Taxis using Environmental Sensors Inproceedings Refereed
In: Streitz, Norbert; Konomi, Shiníchi (Ed.): Distributed, Ambient and Pervasive Interactions, pp. 178–188, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-77015-0.
@inproceedings{taxi_co2_20201,
title = {Towards Infectious Disease Risk Assessment in Taxis using Environmental Sensors},
author = {Hidenaga Ushijima and Shota Ono and Yuuki Nishiyama and Kaoru Sezaki},
editor = {Norbert Streitz and Shiníchi Konomi},
url = {http://2021.hci.international/},
doi = {10.1007/978-3-030-77015-0_13},
isbn = {978-3-030-77015-0},
year = {2021},
date = {2021-07-07},
urldate = {2021-07-07},
booktitle = {Distributed, Ambient and Pervasive Interactions},
volume = {12782},
pages = {178--188},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The spread of Coronavirus disease of 2019 (COVID-19) has reaffirmed the importance of ventilation in enclosed public spaces. Studies on air quality in public spaces such as classrooms, hospitals, and trains have been conducted in the past. However, the interior of a taxi, where an extremely small space is shared with an unspecified number of people, has not been sufficiently studied. This is a unique environment where ventilation is important. This study compared ventilation meth-ods focusing on the CO2 concentration in the cabin, and evaluated the frequency of ventilation in an actual taxi using sensing technology},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Domestic Conference
Sorry, no publications matched your criteria.
Book Chapters
Sorry, no publications matched your criteria.