Empirical-Data-Driven LOS Reclassification via Adaptive Branching Framework for Reflecting Urban Traffic Heterogeneity

Conventional standards for evaluating the Korean Highway Capacity Manual (HCM) and U.S. HCM often inadequately represent the localized macroscopic traffic dynamics inherent in complex urban networks. To address this limitation, this study proposes an adaptive branching framework for level of service (LOS) reclassification, guided by the empirical identifiability of fundamental diagrams (FDs) and vehicular density distribution patterns. The methodology classifies traffic states into four categories: (a) FD-based LOS, (b) segmented FD-based LOS, (c) single-state LOS, and (d) empirical free-flow speed-based LOS. These categories redefine LOS criteria based on the temporal and spatial conditions prevalent in urban environments. The proposed reclassified LOS framework, applied to twenty-eight urban corridors across four distinct urban typologies using a reference free-flow speed, effectively captures region-specific performance variations. Ultimately, this research establishes a robust, data-driven methodological framework for localized LOS recalibration, thereby significantly enhancing the realism of urban traffic evaluation.