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A Spatio-Temporal Approach to Individual Mobility Modeling in On-Device Cognitive Computing Platforms.

Rafael Pérez-Torres1, César Torres-Huitzil2, Hiram Galeana-Zapién3

  • 1School of Computer Science & Information Technology, University College Cork, T12 YN60 Cork, Ireland. rperez@tamps.cinvestav.mx.

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Summary
This summary is machine-generated.

This study introduces an on-device approach for understanding individual mobility patterns using GPS data. It autonomously detects Points of Interest (POIs) and mobility events to build a cognitive map, enhancing mobility prediction and routine analysis.

Keywords:
POIcognitive computinghuman mobilitysmartphonetrajectory

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Area of Science:

  • * Ubiquitous Computing
  • * Data Science
  • * Human Mobility Analysis

Background:

  • * Growing availability of GPS-enabled devices enables collection of location data for mobility-based services (MBS).
  • * Understanding frequent Points of Interest (POIs) and mobility patterns is crucial for analyzing individual behaviors.
  • * Existing methods often rely on crowd data and cloud-assisted processing, which may not be suitable for on-device applications.

Purpose of the Study:

  • * To propose a novel, on-device approach for individual-centered mobility understanding using GPS data.
  • * To autonomously detect Points of Interest (POIs) and mobility events for cognitive map construction.
  • * To develop a spatio-temporal model for characterizing individual mobility dynamics and assessing daily routines.

Main Methods:

  • * Utilizes mobility-based services for on-device data processing.
  • * Employs autonomous detection of POIs and enter-exit mobility events.
  • * Develops an incremental cognitive map (spatio-temporal model) for individual mobility.
  • * Focuses on extracting statistical properties from user-POI interactions.

Main Results:

  • * The proposed spatio-temporal map effectively extracts core features from user-POI interactions.
  • * Demonstrated relevance of extracted features for mobility prediction analytics.
  • * Showcased the exploitation of the spatio-temporal model for assessing daily mobility routine relevance.
  • * The approach is suitable for constrained mobile platforms and conserves energy.

Conclusions:

  • * The novel cognitive and on-line mobility modeling approach enables individual-centered mobility understanding on mobile devices.
  • * Autonomous POI and event detection facilitates the creation of personalized spatio-temporal models.
  • * This contributes to the distributed intelligence of Internet of Things (IoT) devices with low energy impact.