A Preliminary Investigation of Machine Learning Approaches for Mobility Monitoring from Smartphone Data

In this work we investigate the use of machine learning models for the management and monitoring of sustainable mobility, with particular reference to the transport mode recognition. The specific aim is to automatize the detection of the user’s means of transport among those considered in the data collected with an App installed on the users smartphones, i.e. bicycle, bus, train, car, motorbike, pedestrian locomotion. Preliminary results show the potentiality of the analysis for the introduction of reliable advanced, machine learning based, monitoring systems for sustainable mobility.