@article{10.1371/journal.pone.0234003,
    author = {Bjerre-Nielsen, Andreas AND Minor, Kelton AND Sapieżyński, Piotr AND Lehmann, Sune AND Lassen, David Dreyer},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth},
    year = {2020},
    month = {07},
    volume = {15},
    url = {https://doi.org/10.1371/journal.pone.0234003},
    pages = {1-24},
    abstract = {Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications.},
    number = {7},
    doi = {10.1371/journal.pone.0234003}
}