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Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations.

Antonios Karatzoglou^1,2, Dominik Köhler³, Michael Beigl⁴

¹Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany. antonios.karatzoglou@kit.edu.

Sensors (Basel, Switzerland)

|October 27, 2018

Summary

This summary is machine-generated.

Higher-level semantic trajectories improve spatial Markov model accuracy for predicting future locations. A novel Purpose-of-Visit-Dependent Frame (PoVDF) approach enhances prediction by considering location context and outperforms existing models.

Keywords:

context awareness multi-dimensional markov chains semantic location prediction semantic similarity semantic trajectories

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Area of Science:

Computer Science
Artificial Intelligence
Data Science

Background:

Markov Chains are foundational for sequence modeling.
Predicting future locations from semantic trajectories is crucial for various applications.
Existing spatial Markov models face challenges with semantic granularity and contextual data.

Purpose of the Study:

To evaluate the impact of semantic trajectory levels on spatial Markov model performance.
To develop and assess a multi-dimensional Markov Chain incorporating contextual information.
To introduce and validate a novel Purpose-of-Visit-Dependent Frame (PoVDF) for enhanced trajectory prediction.

Main Methods:

Investigated predictive performance across different semantic levels of trajectories.
Developed a multi-dimensional Markov Chain integrating location, time, day, and user activity.
Introduced a Purpose-of-Visit-Dependent Frame (PoVDF) for context-aware location analysis.
Evaluated models against baseline, multi-dimensional Markov Chain, and prefix tree-based models.

Main Results:

Higher semantic levels significantly improve spatial Markov model accuracy.
The multi-dimensional Markov Chain outperforms the baseline but is sensitive to small datasets.
The PoVDF-based approach demonstrates superior performance compared to the multi-dimensional Markov Chain and prefix tree models.
PoVDF effectively captures the dynamic role of locations based on the purpose of visit.

Conclusions:

Semantic level is a critical factor in trajectory modeling accuracy.
The PoVDF approach offers a robust method for location prediction by incorporating contextual dynamics.
PoVDF provides a strong foundation for future research in advanced trajectory analysis and prediction.