Urban mobility is being transformed by the introduction of new technologies such as artificial intelligence (AI), multi agent environments and connected sensors. These technologies can be found in driving assistants, autonomous vehicles and in applications like urban planning as well as mobile healthcare. AI is already used to create better traffic flows in the city and to optimize schedules of public transport and critical services such as ambulance networks.

Designing safe and inclusive future urban mobility solutions requires an interdisciplinary setting, including computer science, political science and law.

MOBIUS is a smartphone-based system for remote tracking of citizens' movements. By collecting smartphone's sensor data such as accelerometer and gyroscope, along with self-report data, the MOBIUS system allows to classify the users' mode of transportation. With the MOBIUS app the users can also activate GPS tracking to visualise their journeys and travelling speed on a map. The MOBIUS app is an example of a tracing app which can provide more insights into how people move around in an urban area.

To further test its validity, we ran an user study collecting data from multiple users. The collected data are used to train an algortim (deep convolutional neural network architecture, which classifies the transportation modes) which reached a mean accuracy of 89%.