Performance Evaluation of Intelligent Networked Intersections based on Hierarchical Analysis and Grey Clustering

With the development of intelligent transportation system, it is of practical significance to consider both Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HDVs) in intersection performance evaluation. By using the evaluation method of hierarchical analysis and gray cluster integration, seven indexes including average vehicle delay, average speed, saturation, conflict point surface density, exhaust emission rate, noise pollution index and information richness were selected to construct an evaluation model for mixed traffic flow characteristics of urban intersections. The comprehensive evaluation results of the performance of the intersections were obtained through simulation data of actual intersections. The rationality and feasibility of the model are verified. In addition, the relationship between connected vehicle penetration and intersection performance in mixed traffic flow is analyzed. The results show that the intersection evaluation level is significantly improved with the increase of connected vehicle CAVs penetration in mixed traffic, while the geometric structure, canal design and signal control remain unchanged, providing a theoretical basis for the development of connected vehicle CAVs.