  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.
