This study investigated the macro and meso mechanisms of void formation in graded aggregates within high-speed railway subgrades under train loads using a hybrid discrete element–finite difference method (DEM-FDM). First, a contact parameter inversion model based on a linear model (LM) was developed using extensive DEM simulations through angle of repose, drop, and inclined plate tests. The contact parameters for graded aggregates were further calibrated through physical and triaxial tests. Next, a refined hybrid DEM-FDM model was established to capture void formation behavior, characterized by the contact force chain ratio, and was validated against field measurements. Finally, simulations were conducted under different levels of void formation to explore the associated mechanisms based on dynamic response and meso-mechanical analysis. The results showed that the LM-based inversion model could accurately determine the contact parameters. The hybrid model’s predictions of dynamic displacement and acceleration under various train speeds fell within the range of the field data. When the fine particle loss ratio l_p was ≤3%, the dynamic displacement and acceleration remained below the standard limits of 0.22 mm and 10 m/s². As l_p increased, the contact between the roadbed and base weakened, and complete separation occurred at l_p ≥ 11%, preventing effective load transfer. These findings offer new insights into void formation in graded aggregates and support the safe operation of high-speed railways.

Signal intersections are key nodes in urban road traffic networks, and real-time queue length information serves as a core performance indicator for formulating effective signal management schemes in modern adaptive traffic signal control systems, thereby enhancing traffic efficiency. In this study, a roadside Light Detection and Ranging (LiDAR) sensor is employed to acquire 3D point cloud data of vehicles in the road space, which acts as an important method for queue length detection. However, during queue-length detection, vehicles in different lanes are prone to occlusion because of the straight-line propagation of laser beams. This paper proposes a queue-length detection method based on variations in vehicle point cloud features to address the occlusion of queue-end vehicles during detection. This method first preprocesses LiDAR point cloud data (including region-of-interest extraction, ground-point filtering, point cloud clustering, object association, and lane recognition) to detect real-time queue lengths across multiple lanes. Subsequently, the occlusion problem is categorized into complete occulusion and partial occlusion, and corresponding processing is performed to correct the detection results. The performance of the proposed queue length detection method was validated through experiments that collected real-world data from three urban road intersections in Suzhou. The results indicate that this method’s average accuracy can reach 99.3%. Furthermore, the effectiveness of the proposed occlusion handling method has been validated through experiments.