Tracking and predicting scheduled vehicle journeys presents several challenges. Efficient tracking requires minimizing costs while maintaining accurate vehicle state correspondence with accuracy guarantees, which depends on effective prediction algorithms. Accurate prediction is difficult as external factors influencing vehicle movements are hard to foresee in advance. Statistical analysis of sparse and incomplete historical tracking data to cluster similar journey patterns and match sub-journeys adds further complexity.