Transportation Mode Detection Using Crowdsourced Smartphone Data

Abstract

Smartphones have gained immense popularity among people and they incorporate several sophisticated capabilities like sensors that broaden the domain of usability. Consequently, smartphone-based crowdsourcing methods show importance as an efficient data acquisition strategy. GPS receiver and sensors like accelerometer and gyroscope can be used to gather data which helps identify the travel mode of the smartphone user. Numerous research projects have been conducted regarding the area of concern and this study uses GPS and Accelerometer data collected from smartphones using crowdsourced methods to develop a robust public transport mode detection approach using machine learning algorithms.