Inferring Private Vehicle Traffic and Congestion from Bus Operations in Regional Cities: Integrating Time-of-Day Utilization and Bus Delay Data

This work proposes a new analytical framework for inferring private vehicle traffic volumes and congestion levels in regional cities using only bus operation data. In many regional contexts, detailed traffic counts, OD matrices, or sensor-based congestion data are unavailable, while bus schedules, revenue records, and delay information are routinely collected. The model leverages time-of-day bus frequency and utilization to estimate total mobility demand, the share of trips served by buses, and the residual demand carried by private vehicles. It further allocates inferred vehicle flows across road links and connects them to standard congestion models such as BPR functions and fundamental diagrams. A key methodological contribution is that bus utilization can be inferred through multiple independent routes, including fare revenue data and observed bus delays, enabling cross-validation and robustness in data-scarce environments. The framework is intentionally simple, transparent, and reproducible, making it suitable both as a practical low-cost estimation tool and as a baseline for validating more complex transportation models. A stylized numerical example illustrates how bus operations can serve as an indirect but informative window into car dependence and congestion patterns in regional cities.