# Semi-supervised GANs to Infer Travel Modes in GPS Trajectories

## TL;DR

This paper introduces semi-supervised GANs for inferring travel modes from GPS trajectories, achieving higher accuracy than CNNs on large-scale real-world data.

## Contribution

It develops and compares semi-supervised GAN architectures for travel mode inference, demonstrating improved accuracy over traditional CNNs.

## Key findings

- Semi-supervised GANs achieved 83.4% accuracy.
- Compared favorably to CNNs and previous studies.
- Utilized large-scale smartphone GPS data from Montreal.

## Abstract

Semi-supervised Generative Adversarial Networks (GANs) are developed in the context of travel mode inference with uni-dimensional smartphone trajectory data. We use data from a large-scale smartphone travel survey in Montreal, Canada. We convert GPS trajectories into fixed-sized segments with five channels (variables). We develop different GANs architectures and compare their prediction results with Convolutional Neural Networks (CNNs). The best semi-supervised GANs model led to a prediction accuracy of 83.4%, while the best CNN model was able to achieve the prediction accuracy of 81.3%. The results compare favorably with previous studies, especially when taking the large-scale real-world nature of the dataset into account.