  As the demand for mobility in our society seems to increase, the various
issues centered on urban mobility are among those that worry most city
inhabitants in this planet. For instance, how to go from A to B in an efficient
(but also less stressful) way? These questions and concerns have not changed
even during the covid-19 pandemic; on the contrary, as the current stand,
people who are avoiding public transportation are only contributing to an
increase in the vehicular traffic. The are of intelligent transportation
systems (ITS) aims at investigating how to employ information and communication
technologies to problems related to transportation. This may mean monitoring
and managing the infrastructure (e.g., traffic roads, traffic signals, etc.).
However, currently, ITS is also targeting the management of demand. In this
panorama, artificial intelligence plays an important role, especially with the
advances in machine learning that translates in the use of computational
vision, connected and autonomous vehicles, agent-based simulation, among
others. In the present work, a survey of several works developed by our group
are discussed in a holistic perspective, i.e., they cover not only the supply
side (as commonly found in ITS works), but also the demand side, and, in an
novel perspective, the integration of both.
