The deployment of Global Positioning System (GPS) sensors in modern smartphones and wearable devices has enabled the acquisition of high-coverage urban trajectories. Extracting knowledge from such diverse spatiotemporal data is essential for optimizing intelligent transportation system operations. Yet a deeper understanding of users' mobility patterns also requires identifying their associated transportation modes. Combined with growing privacy concerns, the considerable effort involved in manual data annotation means that GPS trajectories are in reality not labeled by transportation mode. This poses a significant challenge for machine learning classifiers, which often perform best when trained on large amounts of labeled data. As such, this paper investigates a wide range of time series augmentation methods aiming to improve the real-world applicability of transportation mode identification. In our extensive experiments on Microsoft's Geolife dataset, both discrete wavelet transform and flip augmentations pushed the transportation mode identification accuracy of a convolutional neural network from 85.1% to 87.3% and 87.2%, respectively.