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.