Multi-agent systems require coordination among the agents to solve a given task. For movement on fixed-track networks, traditional scheduling algorithms have dominated so far, but the interest in autonomous and intelligent agents is growing as they promise to react to unexpected and exceptional situations more robustly. In this paper, we study data from the Flatland 2020 NeurIPS Competition, where trains move through a virtual rail network. We developed a timeline-based visualization that provides an overview of all train movements in a simulated episode, clearly hinting at different phases, non-optimal routes, and issues such as deadlocks. This view is complemented with a map view and a graph view, interactively linked through highlighting and synchronous animation. Defining regions of interest in the map builds an analysis graph for detailed inspection. A comparison mode allows contrasting two different episodes regarding the same rail network across all views. We have conducted this application study in close collaboration with the Flatland community. Identified analysis goals stem from interviews with key persons of the community, while the approach itself was developed in two iterations based on feedback from experts with diverse backgrounds. This feedback, together with an analysis of the winning submissions from the competition, confirms that the initial analysis goals can be answered.