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Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities.

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This study introduces a new Adaptive Input Hidden Markov Model (AI-HMM) for analyzing sensor data in smart cities. The AI-HMM effectively captures human behavior patterns and identifies anomalies using low-cost sensors like GPS data.

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probabilistic modellingsmart citytime seriestrajectory analysis

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Area of Science:

  • Smart City Technologies
  • Data Science and Analytics
  • Urban Planning and Human Behavior Analysis

Background:

  • Urban growth necessitates scalable monitoring for city planning, security, and commerce.
  • Current monitoring relies on expensive and privacy-invasive video cameras.
  • Low-cost sensors like Global Positioning System (GPS) offer insights into population behavior but require advanced analysis techniques.

Purpose of the Study:

  • To develop a novel time series analysis technique for understanding human activity from low-dimensional sensor data.
  • To account for contextual influences like time of day and day of the week in behavioral analysis.
  • To introduce the Adaptive Input Hidden Markov Model (AI-HMM) for scalable urban environment monitoring.

Main Methods:

  • Proposed a novel Adaptive Input Hidden Markov Model (AI-HMM).
  • The AI-HMM utilizes multiple transition matrices based on data context.
  • Employs shared adaptive observational models for global data distribution analysis given a latent sequence.

Main Results:

  • Tested the AI-HMM on GPS taxi trajectories and vehicle count data.
  • Demonstrated the model's ability to group distinct behavioral trends.
  • Successfully identified time-specific anomalies in urban mobility patterns.

Conclusions:

  • The AI-HMM provides a flexible and structured approach for analyzing complex sensor data in smart cities.
  • This method offers a privacy-preserving alternative to traditional monitoring systems.
  • AI-HMM enhances the understanding of population behavior for improved urban management.