Jiarui Wang

Adaptive urban traffic management requires timely recognition of changes not only in total traffic volume but also in turning-demand composition. This study develops a structural-entropy framework for identifying sustained changes in turning-demand structural complexity. It further calibrates candidate trigger criteria for structure-adaptive intersection management. The framework defines normalized entropy at the intersection and approach levels and computes adjacent-window entropy differences. Percentile-based thresholds and a two-window persistence constraint are then applied to identify sustained candidate-trigger variables. A case study was conducted using six months of 5 min movement-level turning-flow data from 13 consecutive signalized intersections on an urban arterial corridor, producing 654,346 valid adjacent-window pairs. In the case dataset, the 85th-percentile thresholds were 0.141 at the intersection level and 0.265 at the approach level. The two-window criterion identified 26,955 sustained structural-complexity-change events, whereas a three-window criterion reduced the count to 8434. At the single-window linkage scale, 60.5% of intersection-level high-change windows had synchronous approach-level support, and 47.0% had at least one approach-level leading signal in the previous window. The results indicate that the proposed framework provides an interpretable pre-identification signal for candidate control triggers, which should be combined with operational and safety constraints before implementation.