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Updated: Jul 30, 2025
16:14
Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
13.6K
Pedestrian Flow Identification and Occupancy Prediction for Indoor Areas
Nikolaos Tsiamitros1, Tanmaya Mahapatra2, Ioannis Passalidis3
1Ariadne Maps GmbH, Munich, Brecherspitzstraße 8, 81541 Munich, Germany.
Sensors (Basel, Switzerland)
|May 13, 2023
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.
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.
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