Leveraging the Self-Transition Probability of Ordinal Pattern Transition Graph for Transportation Mode Classification

TL;DR

This paper introduces a novel feature based on the self-transition probability of ordinal pattern transition graphs for classifying transportation modes from GPS trajectory data, outperforming existing entropy-based measures.

Contribution

It is the first to apply Information Theory quantifiers, specifically the self-transition probability, to transportation mode classification from GPS data, demonstrating improved accuracy.

Findings

Self-transition probability outperforms Permutation Entropy and Statistical Complexity.

The approach achieves higher classification accuracy than existing methods.

First application of Information Theory quantifiers in transportation mode classification.

Abstract

The analysis of GPS trajectories is a well-studied problem in Urban Computing and has been used to track people. Analyzing people mobility and identifying the transportation mode used by them is essential for cities that want to reduce traffic jams and travel time between their points, thus helping to improve the quality of life of citizens. The trajectory data of a moving object is represented by a discrete collection of points through time, i.e., a time series. Regarding its interdisciplinary and broad scope of real-world applications, it is evident the need of extracting knowledge from time series data. Mining this type of data, however, faces several complexities due to its unique properties. Different representations of data may overcome this. In this work, we propose the use of a feature retained from the Ordinal Pattern Transition Graph, called the probability of self-transition for transportation mode classification. The proposed feature presents better accuracy results than Permutation Entropy and Statistical Complexity, even when these two are combined. This is the first work, to the best of our knowledge, that uses Information Theory quantifiers to transportation mode classification, showing that it is a feasible approach to this kind of problem.