RT Journal Article
SR Electronic
A1 Filjar, Renato
A1 Sklebar, Ivan
A1 Horvat, Marko
T1 A Comparison of Machine Learning-Based Individual Mobility Classification Models Developed on Sensor Readings from Loosely Attached Smartphones
JF Communications - Scientific Letters of the University of Zilina
YR 2020
VO 22
IS 4
SP 153
OP 162
DO 10.26552/com.C.2020.4.153-162
UL https://komunikacie.uniza.sk/artkey/csl-202004-0019.php
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.