  In this paper we analyze mathematically how human factors can be effectively
incorporated into the analysis and control of complex systems. As an example,
we focus our discussion around one of the key problems in the Intelligent
Transportation Systems (ITS) theory and practice, the problem of speed control,
considered here as a decision making process with limited information
available. The problem is cast mathematically in the general framework of
control problems and is treated in the context of dynamically changing
environments where control is coupled to human-centered automation. Since in
this case control might not be limited to a small number of control settings,
as it is often assumed in the control literature, serious difficulties arise in
the solution of this problem. We demonstrate that the problem can be reduced to
a set of Hamilton-Jacobi-Bellman equations where human factors are incorporated
via estimations of the system Hamiltonian. In the ITS context, these
estimations can be obtained with the use of on-board equipment like
sensors/receivers/actuators, in-vehicle communication devices, etc. The
proposed methodology provides a way to integrate human factor into the solving
process of the models for other complex dynamic systems.
