One of the most desired and challenging services in collective transport systems is the real-time prediction of the near-future travel times of scheduled vehicles, especially public buses, thus improving the experience of the transportation users, who may be able to better schedule their travel, and also enabling system operators to perform real-time monitoring. While travel-time prediction has been researched extensively during the past decade, the accuracies of existing techniques fall short of what is desired, and proposed mathematical prediction models are often not transferable to other systems because the properties of the travel-time-related data of vehicles are highly context-dependent, making the models difficult to fit. We propose a framework for evaluating various predictability types of the data independently of the model, and we also compare predictability analysis results of travel times with the actual prediction errors for real bus trajectories. We have applied the proposed framework to real-time data collected from buses operating in Copenhagen, Denmark.