Towards an optimal driving trains in single line using crossing loops

This paper describes an intelligent approach based on agents that are able to drive and coordinate trains on stretches of railway line containing a crossing loop. Halts close to or even in crossing loops lead to increased consumption of fossil fuels, longer journey times and exhaustion of track capacity. In this paper the agents make use of a set of resources - railway line characteristics, train characteristics, driving rules and information about other trains - to generate their action policy. The agents perception is guaranteed by a set of sensors that provide data such as speed, position and information about the line. The tasks that the agent performs include carrying out actions such as increasing or reducing the speed of the train. The main objective of this study was to avoid unnecessary halts, which are the main cause of increased fuel consumption and journey time. Our results show that strong reductions can be made in terms of fuel consumption (average reduction of 25.5%), journey time (average reduction of 22.5%) and exhaustion of track capacity. Simulations were performed in which traditional driving techniques, with halts at several points along the stretch of track, were compared with driving performed by the multi-agent system, without any halts.