Although time-of-day (TOD) signal operation is widely adopted in urban signal control systems, its boundary settings are often determined empirically without systematic validation. This study presents a network-level, data-driven framework for optimizing TOD boundaries using citywide traffic detector data. One-year traffic volume data collected at 15-min intervals from vehicle detection systems in Daejeon, South Korea, were aggregated to construct a representative daily demand profile. K-means clustering was employed to identify homogeneous temporal traffic states, and candidate TOD boundaries were derived based on cluster transitions. To ensure operational feasibility, a minimum segment length constraint was incorporated. The optimal number of clusters was determined using the silhouette score, resulting in a three-period TOD structure. Compared with a conventional fixed TOD configuration, the proposed approach reduced intra-segment variability by 34.87% in terms of sum of squared errors (SSE) and significantly lowered root mean squared error (RMSE). The results demonstrate that clustering-based TOD boundary optimization enhances temporal homogeneity while maintaining practical applicability for network-level urban signal control.
Seo et al. (2026) studied this question.