research-article

CityFlowFragility: Measuring the Fragility of People Flow in Cities to Disasters using GPS Data Collected from Smartphones

Authors:

Kota Tsubouchi,

Yoshihide SekimotoAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 3

Article No.: 117, Pages 1 - 17

https://doi.org/10.1145/3130982

Published: 11 September 2017 Publication History

Abstract

Economic loss caused by natural disasters is increasing in many cities around the world. There is an increasing demand for a method that effectively measures the fragility of people flow to appropriately plan the future investment into infrastructure. Conventional methods measure the fragility of urban systems using infrastructure data such as the road and railway networks. However, these methods are costly to perform, cannot directly measure the disruption on human activities caused by disasters, nor can they be applied for individual disasters. Here, we propose a novel method that quantifies the fragility of cities through detecting the delay in commuting activities using GPS data collected from smartphones. Because commuting activities are daily routines for many people, commuting flow has little day-to-day fluctuation, which makes it an appropriate metric for detecting anomalies and disruption in urban systems. This method can be utilized in any city in the world regardless of differences in network structures or population distribution, as long as people commute on a daily basis. We validate our method in various cities for snowfall and typhoons using real datasets in Japan, and show that intuitive results can be obtained. Our method's reliability is clarified by comparing the results with conventional metrics. We also present useful analyses and applications of CityFlowFragility for urban planning and disaster management.

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Cited By

Li YLi FYang SZhang CLi YLi FYang SZhang C(2024)A Brief IntroductionIncentive Mechanism for Mobile Crowdsensing10.1007/978-981-99-6921-0_1(1-8)Online publication date: 4-Jan-2024
https://doi.org/10.1007/978-981-99-6921-0_1
He TBao JLi YHe HZheng Y(2023)Crowd-Sensing Enhanced Parking Patrol Using Sharing Bikes’ TrajectoriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313819535:4(3589-3602)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2021.3138195
Mizuno TFujimoto SIshikawa A(2022)Generation of individual daily trajectories by GPT-2Frontiers in Physics10.3389/fphy.2022.102117610Online publication date: 8-Nov-2022
https://doi.org/10.3389/fphy.2022.1021176
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Index Terms

CityFlowFragility: Measuring the Fragility of People Flow in Cities to Disasters using GPS Data Collected from Smartphones
1. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems
      1. Geographic information systems

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 3

September 2017

2023 pages

EISSN:2474-9567

DOI:10.1145/3139486

Issue’s Table of Contents

Copyright © 2017 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 September 2017

Accepted: 01 July 2017

Revised: 01 May 2017

Received: 01 February 2017

Published in IMWUT Volume 1, Issue 3

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Cited By

Li YLi FYang SZhang CLi YLi FYang SZhang C(2024)A Brief IntroductionIncentive Mechanism for Mobile Crowdsensing10.1007/978-981-99-6921-0_1(1-8)Online publication date: 4-Jan-2024
https://doi.org/10.1007/978-981-99-6921-0_1
He TBao JLi YHe HZheng Y(2023)Crowd-Sensing Enhanced Parking Patrol Using Sharing Bikes’ TrajectoriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313819535:4(3589-3602)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2021.3138195
Mizuno TFujimoto SIshikawa A(2022)Generation of individual daily trajectories by GPT-2Frontiers in Physics10.3389/fphy.2022.102117610Online publication date: 8-Nov-2022
https://doi.org/10.3389/fphy.2022.1021176
Fan ZYang CZhang ZSong XLiu YJiang RChen QShibasaki R(2022)Human Mobility-based Individual-level Epidemic Simulation PlatformACM Transactions on Spatial Algorithms and Systems10.1145/34910638:3(1-16)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3491063
Mahmud SShen HZhang Foutz YAnton J(2022)ESEP: Data-Driven Emergency and Safe Evacuation Driving Path Planning During Natural Catastrophes2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020410(1641-1650)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020410
Luca MBarlacchi GLepri BPappalardo L(2021)A Survey on Deep Learning for Human MobilityACM Computing Surveys10.1145/348512555:1(1-44)Online publication date: 23-Nov-2021
https://dl.acm.org/doi/10.1145/3485125
Pang YTsubouchi KYabe TSekimoto Y(2020)Intercity Simulation of Human Mobility at Rare Events via Reinforcement LearningProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422244(293-302)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3397536.3422244
Tsubouchi KTeraoka TGomi HShimosaka M(2020)Parasitic Location Logging: Estimating Users’ Location from Context of Passersby2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom45495.2020.9127381(1-10)Online publication date: Mar-2020
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Raj RRamesh ASeetharam ADeFazio D(2020)SWIFT: A non-emergency response prediction system using sparse Gaussian Conditional Random FieldsPervasive and Mobile Computing10.1016/j.pmcj.2020.101317(101317)Online publication date: Dec-2020
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Wang YLi JZhao XFeng GLuo X(2020)Using Mobile Phone Data for Emergency Management: a Systematic Literature ReviewInformation Systems Frontiers10.1007/s10796-020-10057-wOnline publication date: 16-Sep-2020
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