Evaluating your train simulator. Part II, The task environment

This chapter will expand on this idea, but while the focus of the previous discussion was on the design and classification of the simulator environment, this chapter will focus on the data that are recorded by simulators and how they may be used in view of the simulator’s objectives. In broad terms, train simulators are typically designed for three key purposes, as follows:• Driver training • Competency assessment • Research Using simulators to deliver training allows familiarisation with routes, certain track features and responses to emergency situations in a relatively consequence free setting. Using them to assess competency enables the ongoing measurement of performance. Finally, using them for research enables the systematic investigation of the effects of differences at the driver (e.g. mood, experience, memory, fatigue), train (e.g. type, length, loading, locos, mechanical malfunctions), track (e.g. curvature, length, grade, defects, works, crossings) and environmental (e.g. light, noise, visibility, ice, rain, snow) levels on driving performance. As shown in Figure 9.1, a wide range of factors influence the ability to drive a train, highlighting an information-rich environment. However, simulation requirements, particularly the type and detail of the data required for training, competency and research, are often at odds with one another. The previous chapter made the point that simulators can be used to evaluate more than just technology, and that the fabric of the simulator must also be evaluated if meaningful data are to be derived from the evaluations of humans, systems and the processes placed inside. This chapter will extend this notion but focus more overtly on how these evaluations may be performed for research and review, with a focus on data format, choice of measurement, and interpretation and analysis of data.