research-article

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Using Mobile Sensing Technology for Capturing People Mobility Information

Authors:

Yuuki Nishiyama,

Kaoru SezakiAuthors Info & Claims

PredictGIS'19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility

Pages 33 - 40

https://doi.org/10.1145/3356995.3364541

Published: 05 November 2019 Publication History

Abstract

The detection and analysis of human crowds have been widely used from urban design and traffic management to disaster evacuation and mobility prediction. Currently, several common methods of crowd flow detection have different performances in terms of accuracy, cost and scope of application. One of the main reasons for the difference is that the technology or equipment used to detect crowd flows are different. For example, focus on accuracy, use the camera to record in real-time, and analyze the flow of people with the relevant algorithms. Or pay more attention to the scope of application, using the GPS information uploaded by the user when using the map service to obtain the direction and speed information of the crowd. In this paper, we propose a client-server system based on Bluetooth scanning to obtain crowd information. Our proposed system has the advantages of low cost and location flexibility. The system can detect any area without pre-deployed, as long as there is a sufficient number of users involved.

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Cited By

Hasan RHasan R(2022)X-Fidence: Post-Pandemic Wellness By Density Monitoring with Privacy Preservation2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC49033.2022.9700586(578-583)Online publication date: 8-Jan-2022
https://doi.org/10.1109/CCNC49033.2022.9700586
Alarabi LBasalamah SAbdalla MHendawi A(2021)A Holistic Spatial Platform For Managing Infectious Diseases, Case Study on COVID-19 Pandemic2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671599(3721-3730)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671599
Ukkusuri SSezaki KYabe TTsubouchi K(2020)PredictGIS 2019 workshop report: Held in conjunction with ACM SIGSPATIAL 2019SIGSPATIAL Special10.1145/3383653.338366611:3(34-37)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3383653.3383666
Show More Cited By

Index Terms

Using Mobile Sensing Technology for Capturing People Mobility Information
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Sensor networks

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Information & Contributors

Information

Published In

cover image ACM Conferences

PredictGIS'19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility

November 2019

81 pages

ISBN:9781450369640

DOI:10.1145/3356995

Editors:
Satish V. Ukkusuri
Purdue University, IN, USA
,
Kaoru Sezaki
University of Tokyo, Japan
,
Takahiro Yabe
Purdue University, IN, USA
,
Kota Tsubouchi
Yahoo Japan Corporation, Tokyo, Japan

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

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SIGSPATIAL '19

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SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5, 2019

IL, Chicago, USA

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Cited By

Hasan RHasan R(2022)X-Fidence: Post-Pandemic Wellness By Density Monitoring with Privacy Preservation2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC49033.2022.9700586(578-583)Online publication date: 8-Jan-2022
https://doi.org/10.1109/CCNC49033.2022.9700586
Alarabi LBasalamah SAbdalla MHendawi A(2021)A Holistic Spatial Platform For Managing Infectious Diseases, Case Study on COVID-19 Pandemic2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671599(3721-3730)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671599
Ukkusuri SSezaki KYabe TTsubouchi K(2020)PredictGIS 2019 workshop report: Held in conjunction with ACM SIGSPATIAL 2019SIGSPATIAL Special10.1145/3383653.338366611:3(34-37)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3383653.3383666
Zhou YLau BKoh ZYuen CNg B(2020)Understanding Crowd Behaviors in a Social Event by Passive WiFi Sensing and Data MiningIEEE Internet of Things Journal10.1109/JIOT.2020.29720627:5(4442-4454)Online publication date: May-2020
https://doi.org/10.1109/JIOT.2020.2972062

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