@inproceedings{022d495e766e4f0caa124f06bc833e83,
title = "A Convolutional Neural Network for Transportation Mode Detection Based on Smartphone Platform",
abstract = "Knowledge of people's transportation mode is important in many civilian areas, such as urban transportation planning. Current methodologies in collecting travelers' transportation modes are costly and inaccurate. The increasing sensing and computing capabilities of smartphones and their high penetration rate enable automatic transportation mode detection. This paper designs and implements a light-weight and energy-efficient transportation mode detection application only using the accelerometer sensor on smartphones. In this application, we collect accelerometer data in an efficient way and build a convolutional neural network to determine transportation modes. Different architectures and different classification methods are tested within our convolutional neutral networks in our tests and the best combination is selected for this transportation mode detection application. Performance evaluation shows that the proposed convolutional neural network can achieve the highest accuracy in detecting transportation modes.",
keywords = "accelerometer, deep learning, transportation mode detection",
author = "Xiaoyuan Liang and Guiling Wang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017 ; Conference date: 22-10-2017 Through 25-10-2017",
year = "2017",
month = nov,
day = "14",
doi = "10.1109/MASS.2017.81",
language = "English (US)",
series = "Proceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "338--342",
booktitle = "Proceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017",
address = "United States",
}