An Economic Approach for Generation of Train Driving Plans using Continuous Case-based Planning.

We present an approach for reusing and sharing train driving plans P using continuous (or without human intervention) Case-Based Planning (CBP). P is formed by a set of actions, which when applied, can move a train in a stretch of railroad. This is a complex task due to the variations in the (i) composition of the train, (ii) environmental conditions, and (iii) stretches travelled. To overcome these difficulties we provide to the driver a support system to help the driver in this complex task. CBP was chosen because it allows directly reuse the human drivers experience as well as from other sources. The main steps of the CBP are distributed among specialized agents with different roles: Planner and Executor. Our approach was evaluated by different metrics: (i) accuracy of the case recovery task, (ii) efficiency of task adaptation and application of such cases in realistic scenarios and (iii) fuel consumption. We show that the inclusion of new experiences reduces the efforts of both the Planner and the Executor, reduces significantly the fuel consumption and allow the reuse of the obtained experiences in similar scenarios with low effort.