Method of Extracting Airport Curbside Vehicle Operation Data Based on Point Cloud Data

The aviation hub curbside is the interface between airside and landside transportation systems. Microscopic traffic simulation technology is the most common method to analyze the characteristics of curbside operation. For scientific use of microscopic traffic simulation tools, the operation characteristics of simulation objects should be mastered in detail. In this paper, airport curbside operation characteristic parameter detection method based on radar equipment is proposed. The method is divided into four steps, background point segmentation, trajectory point extraction, trajectory tracking, and traffic information extraction. Firstly, a coordinate system correction is performed by Random Sample Consensus (RANSAC) algorithm. Secondly, the data are clustered and the trajectory points of the vehicle are obtained by Density-Based Spatial Clustering of Application with Noise-Oriented bounding box (DBSCAN-OBB) algorithm, and the trajectory points generated by the same vehicle are tracked to obtain the original vehicle trajectory chain. And remove the outliers in the data sequence and optimize the original trajectory chain by combining median filtering and Kalman filtering. Finally, the vehicle running speed and acceleration parameters are extracted from the trajectory data. In this study, the proposed method was used to obtain a set of curbside parameters for researchers to use, which include the layout of the facilities along the curbside and the operation characteristics data of vehicles and pedestrians.