A Comparison of Machine Learning-Based Individual Mobility Classification Models Developed on Sensor Readings from Loosely Attached Smartphones

https://doi.org/10.26552/com.C.2020.4.153-162

  • Renato Filjar
  • Ivan Sklebar
  • Marko Horvat
Keywords: mobility classification, smartphone, inertial sensor, statistical learning

Abstract

General mobility estimation is demanded for strategy, policy, systems and services developments and operations in transport, urban development and telecommunications. Here is proposed an individual motion readings collection with preserved privacy through loosely fit smartphones, as a novel sole inertial sensors use in commercial-grade smartphones for a wide population data collection, without the need for the new infrastructure and attaching devices. It is shown that the statistical learning-based models of individual mobility classification per means of transport are capable of overcoming the variance introduced by the proposed data collection method. The success of the proposed methodology in a small-scale experiment for the Individual Mobility Classification Model development, using selected statistical learning methods, is demonstrated.

Author Biographies

Renato Filjar

Krapina University of Applied Sciences, Krapina, Croatia

Ivan Sklebar

Catholic University of Croatia, Zagreb, Croatia

Marko Horvat

Department of Applied Computing, Faculty of Engineering and Computing, University of Zagreb, Croatia

Published
2020-10-01
How to Cite
Renato Filjar, Ivan Sklebar, & Marko Horvat. (2020). A Comparison of Machine Learning-Based Individual Mobility Classification Models Developed on Sensor Readings from Loosely Attached Smartphones. Communications - Scientific Letters of the University of Zilina, 22(4), 153-162. https://doi.org/10.26552/com.C.2020.4.153-162
Section
Management Science and Informatics in Transport