Lessons learned from a simulated environment for trains conduction

This paper consolidates and discuss the results of a software agent development, named SDriver, which is able to drive an intercity freight train in a secure, economic and fast way. The SDriver executes a small set of instructions, named: reducing, increasing or maintaining the acceleration point, and start breaking. Three approaches have been studied to implement the core of SDriver: (i) machine learning (classification methods), (ii) distributed constraint optimization, and (iii) specialized rules (if-then). The SDriver performance was evaluated comparing fuel consumption and actions similarity with a real conduction, using a simulated environment. The validation of the knowledge discovered from the machine learning approach was done quantitatively, calculating a degree of similarity between the simulation and the history of travel. The main results are expressed by their mean values: 32% of fuel consumption reduction and 85% action similarity between the SDriver and the real conductor.