A reliable and dynamic Transportation System is the key to broad sustainable mobility of the future. Such a mobility network consists of many different transportation modalities, which need to be integrated seamlessly to offer reliable service to a demand. Constraints from technology, infrastructure, resources, and costs need to be considered.

Such complex and highly connected systems face many different challenges, from traffic flow optimization, addressing the demand by responsive and appropriate scheduling, coordinated maintenance, personnel, and supply chain management. 

Traditionally many of the challenges were solved separately, through experience and human efforts. The introduction of computer systems and classical optimization algorithms allowed to partially improve specific processes. In recent years however, the rise of AI and the simplified access to large datasets have started to influence how problems are solved within the transportation and mobility domain. Modern AI and ML solutions can complement shortcomings of classical optimization algorithms and thus present a viable addition to the current algorithmic solutions. 

In this track we would like to introduce a system-wide discussion on how AI and advanced analytics can play a role to shape the performance of transportation systems and the mobility of the future. This pertains to supervised and unsupervised predictive and prescriptive methods. To this end, the state of the art is balanced with the applied research, and the identification of possible use case, and blockers towards industrial applications.