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Lessons learned from a simulated environment for trains conduction

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7 Author(s)
Sato, D.M.V. ; Pontifical Catholic Univ. of Parana, Brazil ; Borges, A.P. ; Leite, A.R. ; Dordal, O.B.
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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.

Published in:

Industrial Technology (ICIT), 2012 IEEE International Conference on

Date of Conference:

19-21 March 2012