MobilitApp
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About this app
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
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
Updated on
Safety starts with understanding how developers collect and share your data. Data privacy and security practices may vary based on your use, region, and age. The developer provided this information and may update it over time.
No data shared with third parties
Learn more about how developers declare sharing
This app may collect these data types
Location, Personal info, and Device or other IDs
Data is encrypted in transit
Data can’t be deleted
What's new
MobilitApp version 5.0 is out! Redesigned interface, separate environments for data collection and prediction, full Android compatibility, and automatic stop algorithm. Support for various languages has also been added. Update now!
This version has been adapted for a data collection campaign of the UPC community. The campaign runs from 15 to 19 April 2024. To encourage participation, a prize draw will be held to promote sustainable mobility.
This version has been adapted for a data collection campaign of the UPC community. The campaign runs from 15 to 19 April 2024. To encourage participation, a prize draw will be held to promote sustainable mobility.