Nowadays, everything is being built up taking advantage in sensor’s data (e.g.: bridges, computers, houses, vehicles). The Public Transport is not an exception. By being highly dependent on the dynamics of the human behavior (both drivers and passengers), it is intrinsically connected to the data derived from them as well. In the past, this was completely unrealistic. The Data Miners worked closed on their labs with their impractical Machine Learning algorithms – as there was no large-scale data to apply them. On the other hand, the Civil Engineers aimed to model such dynamics assuming theoretical levels of stochasticity and/or optimistic scenarios. Such models comprise a fair but still inaccurate approach to such dynamic behavior. Today’s reality increased dramatically the availability of the mobility-based data by the multiple social infrastructures that already use intelligent sensors and real-time communicational frameworks (e.g. 3G).
The availability of these types of data (e.g. smartphones, traffic light sensors, APC/AVL, fare-based, etc.) on a largescale changed the way both Civil and Computer Scientists faced the problematics around Public Transportation. It enables a whole new bunch of possibilities which are still far by being fully explored. On the other hand, it also brings novel issues regarding each individual’s and/or company’s privacy that are worthy to be discussed and analyzed. Where are we going? Where do we want to go? Which are the current trends? How can we explore these data to improve Public Transportation? What can be done to improve bus transfers coordination? How about the taxi dispatching, preventive maintenance, planning and control of public transportation in general?
These problematics are addressed by this workshop’s scope (introduced below). The researchers/engineers are encouraged to participate and take advantage of this opportunity to exchange ideas and to share their R&D findings/experiences.