Cities are experiencing unparalleled growth, facing new challenges as they endeavor to become healthier, more sustainable and safer places for citizens to live and work. One of such challenges arise in urban mobility and related fields like transport planning and traffic engineering, which are evolving to embrace the myriad of new opportunities that mobile personal devices, together with data science, may bring to citizens. Traffic engineering, for instance, has traditionally been based on stationary sensor data like vehicle counters, induction loops and more recently thermo-cameras to understand core traffic characteristic parameters including flow speed, street saturation and the distribution of cars, trucks, and pedestrian participants. With the ubiquity of mobile personal devices (e.g., wearables, smartphones) and the development of machine learning (ML), however, new opportunities have emerged to collect and analyze mobility data at the granularity of a driver or a pedestrian.