Most transportation mode (i.e., train, car) identification methods can only recognize the activities that were previously seen in the training data. However, they cannot be able to detect an unseen activity without having any corresponding training sample. In this study, we propose a k-shot learning algorithm. When k is set to zero, named zero-shot learning, it can recognize a previously unseen new transportation mode (i.e., bus) even when there are no training samples of that mode in the dataset. The experiments carried out on a real-world dataset showed that the accuracy rates from 89.46% to 93.94% were achieved by the proposed method with different values of parameter k. The results also showed that our method outperformed the state-of-the-art methods in terms of classification accuracy.