MobilitApp periodically collects data from 3 mobile sensors: accelerometer, magnetometer and gyroscope. We do not use GPS to predict the means of transport, this way we save battery life and do not read the person's geolocation constantly.

The information obtained is processed to train a machine learning model capable of subsequently predicting the means of transportation used by citizens. Currently MobilitApp recognizes these means of transport with more than 88% accuracy: bicycle, electric bicycle, electric scooter, bus, subway, train, tram, motorcycle, car, running and walking.

Users can view the transport mode prediction on the map for each section of the multimodal route while they move.

User information is treated completely anonymously.

MobilitApp collects information about the multimodal journey: where it starts, with what means of transport, where a change in the means of transport is detected, what is the new means of transport. And so on, section by section, until the end of the multimodal route is detected.

No identifying information of the user is collected. The objective is to collect a database of multimodal trajectories of citizenship in the metropolitan area, in order to analyze the flow of mobility and the mobility habits of citizens.

MobilitApp is a tool that aims to help the authorities in charge of public transport to improve the service offered. Our goal is to collaborate in achieving sustainable urban mobility and for Barcelona to be a city that is increasingly pollution-free and more livable.

MobilitApp is being developed by researchers and students from the SISCOM (https://siscom.upc.edu) research group, in the Department of Telematic Engineering (http://www.entel.upc.edu) of the Universitat Politècnica de Catalunya (https://www.upc.edu), with the supervision of prof. Monica Aguilar Igartua.

MobilitApp web page: https://mobilitapp.upc.edu

MobilitApp

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