The present study highlights a novel concept for estimating the performability of automatic or unattended train operation. This need has been identified through supporting strategies concerning the implementation of Automatic Train Operation (ATO) atop diverse Grade of Automation (GoA). In addressing this challenge, the handling of delay logs supplied by train dispatchers remain a major concern as it can easily spread over the network. This task requires an integrated approach to detect, transmit and resolve difficulties caused by unplanned events. Therefore, we addressed the detectability aspects through the introduction a Joint Cognitive System (JCS) approach relying on a context-aware methodology with the aim to maximize the train driver interaction and resolution of performability issues with technical and safety competency. We proposed a Novelty Detection K-means algorithm for defining with sensory measurements, potential unplanned events and visual image processing for handling track related abnormalities on higher GoA levels. For transmission, we integrated the JCS concept in mainline railway and its context with satellite-based communication. This approach was incorporated into an optimization performability framework for mitigating uncertainty on critical dependability parameters. The validation strategy was exemplified by means of a model-based evaluation compliant with operational and regulatory authority demands.