Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth.

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 acc

SUBMITTER: Bjerre-Nielsen A 

PROVIDER: S-EPMC7332005 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature