Classifying Transport Mode from Global Positioning Systems and Accelerometer Data: A Machine Learning Approach

Abstract

Smartphones and wearable devices are driving a boom in mobility data. We use data-driven tools for classifying movement data into five different travel modes (bicycle, walk, bus, motor vehicle and SkyTrain) in Vancouver and St. John’s, Canada. Using data from a GPS-enabled smartphone app (Itinerum) combined with a wrist-worn accelerometer (GENEActiv) collected over a period of 67 days, we classified modes using Support Vector Machines from 4071 trips. Pre-labelled data were used to classify modes with 90.9% accuracy when data from both devices were combined in comparison to a single data source with accuracy ranging between 55.5% and 79.4%.