A convolutional neural networks based transportation mode identification algorithm

With the increasing sensing ability of smartphone, both recognizing and understanding a user's activity using sensor data have become a popular topic of ubiquitous computing systems. Individual transportation mode identification can provide essential data for road planning and traffic management. In this paper, we present a Convolutional Neural Networks (CNN) based method to extract expressive and discriminative features automatically for transportation mode identification. The signal preprocessing in the time and frequency domain is performed before the sensor data is fed into the deep learning framework. We optimize various important hyper-parameters such as learning rate, kernel size and number of convolutional layers to adapt the characteristics of multiple sensor signals. Extensive experimental results indicate that the proposed CNN based transportation mode identification algorithm can achieve 98% accuracy to distinguish between car, bus, train and metro, which outperforms the Support Vector Machines and Adaboost based transportation identification with better robustness and generalization.