A Planning-Level Traffic Estimation Framework for Data-Scarce Regional Cities: Integrating Urban Form, Population Distribution, and Bus Operations Data

This work proposes a lightweight and transferable framework for estimating planning-level traffic volumes in data-scarce regional cities. The method integrates (i) urban form and residential population distribution, (ii) a first-order approximation model that infers time-varying mobility demand from bus operations data, and (iii) a simple network assignment procedure to reconstruct link-level traffic volumes. Building on the mathematical approximation theory developed in Student (2026), the framework provides explicit conditions for stability, Lipschitz continuity, and error propagation. The results show that total traffic demand can be estimated within ±10% and link-level volumes within ±20–30%, which is sufficient for strategic planning due to the threshold nature of congestion and discrete policy interventions. This research establishes a mathematically justified and practically feasible foundation for mobility planning in regional cities lacking continuous traffic counts or sensor networks.