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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Summary

This study enhances indoor analytics using Wi-Fi probe requests for precise location tracking. It compares Prophet and Autoregressive Moving Average (ARMA) models to understand human behavior and pedestrian flow in a retail environment.

Keywords:
ARMA modelProphet modelindoor localizationindoor positioningpedestrian flow analysis

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Area of Science:

  • Computer Science
  • Data Analytics
  • Human Behavior Analysis

Background:

  • Indoor localization is crucial for tracking within buildings where GPS is ineffective.
  • Smart devices generate Wi-Fi probe requests containing spatiotemporal data.
  • Understanding human movement patterns is vital for retail analytics.

Purpose of the Study:

  • To improve analytics research using indoor localization data.
  • To compare the Prophet and Autoregressive Moving Average (ARMA) models for time-series forecasting.
  • To analyze human behavior and pedestrian flow within a live retail store.

Main Methods:

  • Utilized Wi-Fi probe requests for indoor localization.
  • Implemented and compared the Prophet and ARMA models for data analysis.
  • Mapped probe requests to store sections for spatial awareness.
  • Conducted pedestrian flow analysis to identify common movement paths.

Main Results:

  • The Prophet model demonstrated ease of use in handling outliers and missing data.
  • The ARMA model offered greater customization for personalized results.
  • Identified common pedestrian pathways within the retail space.
  • Successfully forecasted usage patterns using historical data.

Conclusions:

  • Wi-Fi probe requests are a valuable data source for indoor analytics and human behavior studies.
  • Both Prophet and ARMA models have distinct advantages for time-series forecasting in this context.
  • The findings provide insights into optimizing store layout and customer experience.