  This paper discusses new techniques to enhance Automated Transit Networks
(ATN, previously called Personal Rapid Transit - PRT) based on Artificial
Intelligence tools. The main direction is improvement of the cooperation of
autonomous modules that use negotiation protocols, following the IoT paradigm.
One of the goals is to increase ATN system throughput by tuning up autonomous
vehicles cooperation. Machine learning (ML) was used to improve algorithms
designed by human programmers. We used "existing controls" corresponding to
near-optimal solutions and built refinement models to more accurately relate a
system's dynamics to its performance. A mechanism that mostly influences ATN
performance is Empty Vehicle Management (EVM). The algorithms designed by human
programmers was used: calls to empty vehicles for waiting passengers and
balancing based on reallocation of empty vehicles to achieve better regularity
of their settlement. In this paper we discuss how we can improve these
algorithms (and tune them to current conditions) by using ML to tailor
individual behavioral policies. Using ML techniques was possible because our
algorithm is based on a set of parameters. A number of weights and thresholds
could be tuned up to give better decisions on moving empty vehicles across the
track.
