  While the development of fully autonomous vehicles is one of the major
research fields in the Intelligent Transportation Systems (ITSs) domain, the
upcoming longterm transition period - the hybrid vehicular traffic - is often
neglected. However, within the next decades, automotive systems with
heterogeneous autonomy levels will share the same road network, resulting in
new problems for traffic management systems and communication network
infrastructure providers. In this paper, we identify key challenges of the
upcoming hybrid traffic scenario and present a system-of-systems model, which
brings together approaches and methods from traffic modeling, data science, and
communication engineering in order to allow data-driven traffic flow
optimization. The proposed model consists of data acquisition, data transfer,
data analysis, and data exploitation and exploits real world sensor data as
well as simulative optimization methods. Based on the results of multiple case
studies, which focus on individual challenges (e.g., resource-efficient data
transfer and dynamic routing of vehicles), we point out approaches for using
the existing infrastructure with a higher grade of efficiency.
