Nowcasting the vehicular delay at intersections of road networks not only optimizes the signal timing at the intersections, but also alleviates traffic congestion effectively. Existing research work on the vehicular delay nowcasting involves two issues: low effectiveness on low-ping frequency trajectory data, and low efficiency for the nowcasting task. Inspired by recent works on hypergraphs which explore the high-order relationship of trajectory points, we propose an incremental hypergraph learning framework for nowcasting the control delay of vehicles from low-ping frequency trajectories. The framework characterizes the relationship among trajectory points using multi-kernel learning of multiple attributes of trajectory points. Then, it predicts the unknown trajectory points by incrementally constructing hypergraphs of both observed and unknown points and examining the total similarities of hyperedges associated with all the points. Finally, it evaluates the control delay of each trajectory precisely and efficiently based on the timestamp difference of critical points. We conduct experiments on the Didi-Chengdu dataset with 10-second ping frequency. Our framework outperforms state-of-the-art methods in both the accuracy and efficiency (with 6 seconds at each intersection averagely) for the control delay nowcasting task. That facilitates our framework for many real-world traffic scenarios.