  Cyber-Physical Systems in general, and Intelligent Transport Systems (ITS) in
particular use heterogeneous data sources combined with problem solving
expertise in order to make critical decisions that may lead to some form of
actions e.g., driver notifications, change of traffic light signals and braking
to prevent an accident. Currently, a major part of the decision process is done
by human domain experts, which is time-consuming, tedious and error-prone.
Additionally, due to the intrinsic nature of knowledge possession this decision
process cannot be easily replicated or reused. Therefore, there is a need for
automating the reasoning processes by providing computational systems a formal
representation of the domain knowledge and a set of methods to process that
knowledge. In this paper, we propose a knowledge model that can be used to
express both declarative knowledge about the systems' components, their
relations and their current state, as well as procedural knowledge representing
possible system behavior. In addition, we introduce a framework for knowledge
management and automated reasoning (KMARF). The idea behind KMARF is to
automatically select an appropriate problem solver based on formalized
reasoning expertise in the knowledge base, and convert a problem definition to
the corresponding format. This approach automates reasoning, thus reducing
operational costs, and enables reusability of knowledge and methods across
different domains. We illustrate the approach on a transportation planning use
case.
