Emerging mobility-aware content delivery approaches are being proposed to cope with the increasing usage of data from vehicular users. The main idea is to forecast the user locations and associated link capacity, and then proactively counter service fluctuations in advance. For instance, a user that is heading towards low coverage can be prioritized and have video content prebuffered. While the reported gains are encouraging, the results are primarily based on assumptions of perfect prediction. Investigating the predictability of mobility and future signal variations is therefore imperative to evaluate the practical viability of such predictive content delivery paradigms. To this end, this paper presents a large-scale measurement study of 33 repeated trips along a 23.4 km bus route covering urban and sub-urban areas in Kingston, Canada. We provide a thorough analysis of the collected traces to investigate the effects of geographical area, time, forecasting window, and contextual factors such as signal lights and bus stops. The collected dataset can also be used in several other ways to further investigate and drive research in predictive vehicular content delivery.