[1]中小城市交通安全KAP关系的结构方程模型分析
AcceptedThis work investigates the structural relationships among knowledge, attitude, and practice in traffic safety for small and medium-sized cities using structural equation modeling.
Undergraduate Student · Transportation Engineering · Guangzhou Maritime University
Yu YE is an undergraduate student majoring in Transportation Engineering at Guangzhou Maritime University. His research interests include traffic flow theory, transportation planning and management, intelligent transportation systems, mixed traffic flow modeling, traffic safety, and UAV path planning under dynamic disturbances.
This work investigates the structural relationships among knowledge, attitude, and practice in traffic safety for small and medium-sized cities using structural equation modeling.
This study focuses on fundamental diagram modeling and simulation for mixed traffic flow under functional degradation, aiming to support traffic flow analysis in heterogeneous driving environments.
The manuscript studies disturbance-resistant UAV path planning in maritime environments and explores reinforcement-learning-based planning under dynamic disturbances.
The manuscript examines mixed traffic flow modeling under functional degradation and develops a simulation-oriented fundamental diagram analysis framework.
The patent proposes a multidimensional self-sensing device and method for global-local instability perception of triboelectric roadbed systems.
Focused on mixed traffic flow modeling, fundamental diagram analysis, and simulation under functional degradation, with attention to heterogeneous traffic states and intelligent transportation applications.
Conducted research on traffic safety behavior mechanisms using the KAP framework and structural equation modeling, aiming to support traffic safety evaluation and management in small and medium-sized cities.
Explored disturbance-resistant path planning for maritime UAV operations, including dynamic scenario modeling, risk-aware planning, and reinforcement-learning-based trajectory optimization.