Design an artificially intelligent automaton for the real-world dynamics of the Amtrak rail road track

This paper demonstrates the feasibility of employing an artificially intelligent automaton to the task of monitoring Amtrak rail-road track system in a real time transportation environment. The neural net-based system (automaton) processes several quantities that portray the localized existence of the Amtrak system. These quantities may be one or more of location of the switch on the rail-road track, switch number, time of observation, and the switch name. Given these quantities, it is desired that the state of the system, (which can only belong to one of several distinct categories) be predicted as outputs of the automaton. Possible outputs are conditions classified as NORMAL, NOT NORMAL, REVERSE, and NOT REVERSE. Implicit in the choice of a configuration of inputs and outputs is the hypothesis of the existence of a multi-variable mapping connecting these inputs and outputs-a mapping that hopefully coincides with the real-world dynamics of the rail-road track. The neural net-based system is tested on a specific, already in place transportation control system-the centralized electrification & traffic control (CETC) system operated by Amtrak on the northeast corridor. The CETC is chosen because the clear value, which such an operational safety and security monitor would bring to it. The test results obtained in this paper confirm that artificial neural networks can be effectively used to solve the pattern recognition problem posed by Amtrak system. To the best of the author´s knowledge no similar work is outstanding, planned or anticipated at this time.