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

Using Machine Learning to Enhance Vehicles Traffic in ATN (PRT) Systems