Predicting Transportation Modes of GPS Trajectories using Feature Engineering and Noise Removal

TL;DR

This paper presents a comprehensive framework for predicting transportation modes from GPS data, emphasizing feature engineering and noise removal to achieve high accuracy and outperform existing methods.

Contribution

The paper introduces a novel framework with new feature extraction techniques and noise removal steps that improve transportation mode prediction accuracy.

Findings

Achieved 96.5% accuracy and 96.3% F1 score.

Noise removal significantly improves model performance.

Outperforms most state-of-the-art methods.

Abstract

Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict transportation modes. This framework follows a sequence of five steps: (i) data preparation, where GPS points are grouped in trajectory samples; (ii) point features generation; (iii) trajectory features extraction; (iv) noise removal; (v) normalization. We show that the extraction of the new point features: bearing rate, the rate of rate of change of the bearing rate and the global and local trajectory features, like medians and percentiles enables many classifiers to achieve high accuracy (96.5%) and f1 (96.3%) scores. We also show that the noise removal task affects the performance of all the models tested. Finally, the empirical tests where we compare this work against state-of-art transportation mode prediction strategies show that our framework is competitive and outperforms most of them.