Traffic Simulation-Based Sensitivity Analysis of Long Underground Expressways

Long underground expressways have emerged as an alternative to surface highways in densely urbanized areas; however, their enclosed geometry, extended length, and steep longitudinal gradients introduce traffic-flow dynamics distinct from those of surface roads. This study investigates the combined and interaction effects of traffic volume, heavy-vehicle ratio, longitudinal gradient, lane number, and lane-changing policy on traffic performance in long underground expressways using microscopic traffic simulation. A hypothetical 20 km underground expressway network was evaluated under 72 systematically designed scenarios. Weighted average speed and throughput were analyzed using nonparametric statistics, generalized linear models with interaction terms, and machine learning-based sensitivity analysis. While traffic volume and heavy-vehicle ratio were confirmed as dominant determinants of performance, a key contribution of this study is the identification of the density-dependent role of lane-changing policies. Under moderate traffic density, permissive lane-changing improves efficiency by enabling vehicles to bypass localized disturbances caused by heavy vehicles and longitudinal gradients, thereby enhancing capacity utilization. In contrast, under high-density conditions, permissive lane-changing amplifies lane-change conflicts and shockwave propagation within the confined underground environment, accelerating traffic instability and performance breakdown. These adverse effects are further intensified by steep uphill gradients. The findings demonstrate that lane-changing policies on long underground expressways should be designed in a context-sensitive manner, balancing efficiency and stability across traffic states.