Particle filter for real-time human mobility prediction following unprecedented disaster

Akihito Sudo; Takehiro Kashiyama; Takahiro Yabe; Hiroshi Kanasugi; Xuan Song; Tomoyuki Higuchi; Shin'Ya Nakano; Masaya Saito; Yoshihide Sekimoto

doi:10.1145/2996913.2997000

Particle filter for real-time human mobility prediction following unprecedented disaster

Akihito Sudo, Takehiro Kashiyama, Takahiro Yabe, Hiroshi Kanasugi, Xuan Song, Tomoyuki Higuchi, Shin'Ya Nakano, Masaya Saito, Yoshihide Sekimoto

Technology Management and Innovation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Real-time estimation of human mobility following a massive disaster will play a crucial role in disaster relief. Because human mobility in massive disasters is quite different from their usual mobility, real-time human location data is necessary for precise estimation. Due to privacy concerns, real-time data is anonymized and a popular form of anonymization is population distribution. In this paper, we aim to estimate human mobility following an unprecedented disaster using such population distribution data. To overcome technical obstacles including high dimensionality, we propose novel particle filter by devising proposal distribution. Our proposal distribution provides states considering both prediction model and acquired observation. Therefore, particles maintain high likelihood. In the experiments, our methods realized more accurate estimation than the baselines, and its estimated mobility was consistent with the survey researches. The computational cost is significantly low enough for real-time operations. The GPS data collected on the day of the Great East Japan Earthquake is used for the evaluation.

Original language	English (US)
Title of host publication	24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
Editors	Matthias Renz, Mohamed Ali, Shawn Newsam, Matthias Renz, Siva Ravada, Goce Trajcevski
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450345897
DOIs	https://doi.org/10.1145/2996913.2997000
State	Published - Oct 31 2016
Event	24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 - Burlingame, United States Duration: Oct 31 2016 → Nov 3 2016

Publication series

Name	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference	24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
Country/Territory	United States
City	Burlingame
Period	10/31/16 → 11/3/16

Keywords

Bayesian inference
Disaster management
GPS data
Human mobility prediction

ASJC Scopus subject areas

Earth-Surface Processes
Computer Science Applications
Modeling and Simulation
Computer Graphics and Computer-Aided Design
Information Systems

Access to Document

10.1145/2996913.2997000

Cite this

Sudo, A., Kashiyama, T., Yabe, T., Kanasugi, H., Song, X., Higuchi, T., Nakano, SY., Saito, M., & Sekimoto, Y. (2016). Particle filter for real-time human mobility prediction following unprecedented disaster. In M. Renz, M. Ali, S. Newsam, M. Renz, S. Ravada, & G. Trajcevski (Eds.), 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 Article 5 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). Association for Computing Machinery. https://doi.org/10.1145/2996913.2997000

Particle filter for real-time human mobility prediction following unprecedented disaster. / Sudo, Akihito; Kashiyama, Takehiro; Yabe, Takahiro et al.
24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. ed. / Matthias Renz; Mohamed Ali; Shawn Newsam; Matthias Renz; Siva Ravada; Goce Trajcevski. Association for Computing Machinery, 2016. 5 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sudo, A, Kashiyama, T, Yabe, T, Kanasugi, H, Song, X, Higuchi, T, Nakano, SY, Saito, M & Sekimoto, Y 2016, Particle filter for real-time human mobility prediction following unprecedented disaster. in M Renz, M Ali, S Newsam, M Renz, S Ravada & G Trajcevski (eds), 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016., 5, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Association for Computing Machinery, 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016, Burlingame, United States, 10/31/16. https://doi.org/10.1145/2996913.2997000

Sudo A, Kashiyama T, Yabe T, Kanasugi H, Song X, Higuchi T et al. Particle filter for real-time human mobility prediction following unprecedented disaster. In Renz M, Ali M, Newsam S, Renz M, Ravada S, Trajcevski G, editors, 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. Association for Computing Machinery. 2016. 5. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). doi: 10.1145/2996913.2997000

Sudo, Akihito ; Kashiyama, Takehiro ; Yabe, Takahiro et al. / Particle filter for real-time human mobility prediction following unprecedented disaster. 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. editor / Matthias Renz ; Mohamed Ali ; Shawn Newsam ; Matthias Renz ; Siva Ravada ; Goce Trajcevski. Association for Computing Machinery, 2016. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

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abstract = "Real-time estimation of human mobility following a massive disaster will play a crucial role in disaster relief. Because human mobility in massive disasters is quite different from their usual mobility, real-time human location data is necessary for precise estimation. Due to privacy concerns, real-time data is anonymized and a popular form of anonymization is population distribution. In this paper, we aim to estimate human mobility following an unprecedented disaster using such population distribution data. To overcome technical obstacles including high dimensionality, we propose novel particle filter by devising proposal distribution. Our proposal distribution provides states considering both prediction model and acquired observation. Therefore, particles maintain high likelihood. In the experiments, our methods realized more accurate estimation than the baselines, and its estimated mobility was consistent with the survey researches. The computational cost is significantly low enough for real-time operations. The GPS data collected on the day of the Great East Japan Earthquake is used for the evaluation.",

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AU - Kashiyama, Takehiro

AU - Yabe, Takahiro

AU - Kanasugi, Hiroshi

AU - Song, Xuan

AU - Higuchi, Tomoyuki

AU - Nakano, Shin'Ya

AU - Saito, Masaya

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N2 - Real-time estimation of human mobility following a massive disaster will play a crucial role in disaster relief. Because human mobility in massive disasters is quite different from their usual mobility, real-time human location data is necessary for precise estimation. Due to privacy concerns, real-time data is anonymized and a popular form of anonymization is population distribution. In this paper, we aim to estimate human mobility following an unprecedented disaster using such population distribution data. To overcome technical obstacles including high dimensionality, we propose novel particle filter by devising proposal distribution. Our proposal distribution provides states considering both prediction model and acquired observation. Therefore, particles maintain high likelihood. In the experiments, our methods realized more accurate estimation than the baselines, and its estimated mobility was consistent with the survey researches. The computational cost is significantly low enough for real-time operations. The GPS data collected on the day of the Great East Japan Earthquake is used for the evaluation.

AB - Real-time estimation of human mobility following a massive disaster will play a crucial role in disaster relief. Because human mobility in massive disasters is quite different from their usual mobility, real-time human location data is necessary for precise estimation. Due to privacy concerns, real-time data is anonymized and a popular form of anonymization is population distribution. In this paper, we aim to estimate human mobility following an unprecedented disaster using such population distribution data. To overcome technical obstacles including high dimensionality, we propose novel particle filter by devising proposal distribution. Our proposal distribution provides states considering both prediction model and acquired observation. Therefore, particles maintain high likelihood. In the experiments, our methods realized more accurate estimation than the baselines, and its estimated mobility was consistent with the survey researches. The computational cost is significantly low enough for real-time operations. The GPS data collected on the day of the Great East Japan Earthquake is used for the evaluation.

KW - Bayesian inference

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KW - GPS data

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ER -

Particle filter for real-time human mobility prediction following unprecedented disaster

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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