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Mobility episode detection from CDR's data using switching Kalman filter

Authors:

Oleg Batrashev,

Eero VainikkoAuthors Info & Claims

MobiGIS '15: Proceedings of the Fourth ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

Pages 63 - 69

https://doi.org/10.1145/2834126.2834139

Published: 03 November 2015 Publication History

Abstract

The detection of stay-jump-and-moving movement episodes using only cellular data is a big challenge due to the nature of the data. In this article, we propose a method to automatically detect the movement episodes (stay-jump-and-moving) from sparsely sampled spatio-temporal data, in our case Call Detail Records (CDRs), using switching Kalman filter with a new integrated movement model and cellular coverage optimization approach. The algorithm is capable of estimating the movement episodes and classifying the trajectory sequences associated to a stay, a jump or a moving action. The result of this approach can be beneficial for applications using cellular data related to traffic management, mobility profiling, and semantic enrichment.

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Cited By

Lind AWu SHadachi A(2023)Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio NetworkSensors10.3390/s2307360323:7(3603)Online publication date: 30-Mar-2023
https://doi.org/10.3390/s23073603
Komisarenko VVoormansik KElshawi RSakr S(2022)Exploiting time series of Sentinel-1 and Sentinel-2 to detect grassland mowing events using deep learning with reject regionScientific Reports10.1038/s41598-022-04932-612:1Online publication date: 19-Jan-2022
https://doi.org/10.1038/s41598-022-04932-6
Dyrmishi SHadachi A(2021)Mobile Positioning and Trajectory Reconstruction Based on Mobile Phone Network Data: A Tentative Using Particle Filter2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)10.1109/MT-ITS49943.2021.9529277(1-7)Online publication date: 16-Jun-2021
https://doi.org/10.1109/MT-ITS49943.2021.9529277
Show More Cited By

Index Terms

Mobility episode detection from CDR's data using switching Kalman filter
1. Mathematics of computing
  1. Probability and statistics
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

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Published In

cover image ACM Conferences

MobiGIS '15: Proceedings of the Fourth ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

November 2015

95 pages

ISBN:9781450339773

DOI:10.1145/2834126

Editors:
Chi-Yin Chow
City University of Hong Kong, Hong Kong
,
Maria Luisa Damiani
University of Milan, Italy
,
Shashi Shekhar
University of Minnesota - Twin Cities

Copyright © 2015 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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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2015

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SIGSPATIAL

SIGSPATIAL'15: 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 3, 2015

Washington, Bellevue

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Cited By

Lind AWu SHadachi A(2023)Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio NetworkSensors10.3390/s2307360323:7(3603)Online publication date: 30-Mar-2023
https://doi.org/10.3390/s23073603
Komisarenko VVoormansik KElshawi RSakr S(2022)Exploiting time series of Sentinel-1 and Sentinel-2 to detect grassland mowing events using deep learning with reject regionScientific Reports10.1038/s41598-022-04932-612:1Online publication date: 19-Jan-2022
https://doi.org/10.1038/s41598-022-04932-6
Dyrmishi SHadachi A(2021)Mobile Positioning and Trajectory Reconstruction Based on Mobile Phone Network Data: A Tentative Using Particle Filter2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)10.1109/MT-ITS49943.2021.9529277(1-7)Online publication date: 16-Jun-2021
https://doi.org/10.1109/MT-ITS49943.2021.9529277
Lind AHadachi A(2021)Towards state-full positioning of mobile subscribers through advanced cell coverage modeling technique2021 International Conference on Localization and GNSS (ICL-GNSS)10.1109/ICL-GNSS51451.2021.9452272(1-6)Online publication date: 1-Jun-2021
https://doi.org/10.1109/ICL-GNSS51451.2021.9452272
Kannangara SXie HTanin EHarwood AKarunasekera S(2020)Tracking Group Movement in Location Based Social NetworksProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422211(251-262)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3397536.3422211
Vajakas TKiis THadachi AVainikko E(2018)Mobility Episode Discovery in the Mobile Networks Based on Enhanced Switching Kalman Filter2018 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)10.1109/ICUMT.2018.8631264(1-7)Online publication date: Nov-2018
https://doi.org/10.1109/ICUMT.2018.8631264
Hadachi ALind ALomps JPiksarv P(2018)From Mobility Analysis to Mobility Hubs Discovery: A Concept Based on Using CDR Data of the Mobile Networks2018 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)10.1109/ICUMT.2018.8631200(1-6)Online publication date: Nov-2018
https://doi.org/10.1109/ICUMT.2018.8631200

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