PT  - JOURNAL ARTICLE
AU  - Filjar, Renato
AU  - Sklebar, Ivan
AU  - Horvat, Marko
TI  - A Comparison of Machine Learning-Based Individual Mobility Classification Models Developed on Sensor Readings from Loosely Attached Smartphones
DP  - 2020 Oct 1
TA  - Communications - Scientific Letters of the University of Zilina
PG  - 153--162
VI  - 22
IP  - 4
AID - 10.26552/com.C.2020.4.153-162
IS  - 13354205
AB  - 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.