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Anomalous event detection on large-scale GPS data from mobile phones using hidden markov model and cloud platform

Authors:

Apichon Witayangkurn,

Teerayut Horanont,

Yoshihide Sekimoto,

Ryosuke ShibasakiAuthors Info & Claims

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

Pages 1219 - 1228

https://doi.org/10.1145/2494091.2497352

Published: 08 September 2013 Publication History

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Abstract

Anomaly detection is an important issue in various research fields. An uncommon trajectory or gathering of people in a specific area might correspond to a special event such as a festival, traffic accident or natural disaster. In this paper, we aim to develop a system for detecting such anomalous events in grid-based areas. A framework based on a hidden Markov model is proposed to construct a pattern of spatio-temporal movement of people in each grid during each time period. The numbers of GPS points and unique users in each grid were used as features and evaluated. We also introduced the use of local score to improve the accuracy of the event detection. In addition, we utilized Hadoop, a cloud-computing platform, to accelerate the processing speed and allow the handling of large-scale data. We evaluated the system using a dataset of GPS trajectories of 1.5 million individual mobile phone users accumulated over a one-year period, which constitutes approximately 9.2 billion records.

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https://doi.org/10.1109/JIOT.2024.3371482
Fährmann DMartín LSánchez LDamer N(2024)Anomaly Detection in Smart Environments: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2024.339505112(64006-64049)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3395051
Liu MYang KFu YWu DDu W(2023)Driving Maneuver Anomaly Detection Based on Deep Auto-Encoder and Geographical PartitioningACM Transactions on Sensor Networks10.1145/356321719:2(1-22)Online publication date: 17-Apr-2023
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Anomalous event detection on large-scale GPS data from mobile phones using hidden markov model and cloud platform
1. Information systems

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Published In

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

September 2013

1608 pages

ISBN:9781450322157

DOI:10.1145/2494091

General Chairs:
Friedemann Mattern
ETH Zurich, CH
,
Silvia Santini
TU Darmstadt, DE
,
Program Chairs:
John F. Canny
UC Berkeley, US
,
Marc Langheinrich
Università della Svizzera italiana, CH
,
Jun Rekimoto
University of Tokyo, JP

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

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Published: 08 September 2013

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University of Florida
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UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 8 - 12, 2013

Zurich, Switzerland

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UbiComp '13 Adjunct Paper Acceptance Rate 254 of 399 submissions, 64%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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https://doi.org/10.1109/JIOT.2024.3371482
Fährmann DMartín LSánchez LDamer N(2024)Anomaly Detection in Smart Environments: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2024.339505112(64006-64049)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3395051
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