Vehicles comprise several critical systems, including the braking, steering, transmission, and suspension systems, which operate in concert to ensure safe and efficient movement. Research has established that vehicle malfunctions, particularly in the braking system, contribute significantly to road accidents, with technical failures accounting for approximately 15% of crashes and brake system failures responsible for 17.4% of these incidents. In light of this, an investigation was conducted to identify the factors that influence the braking coefficient and the variability of braking force in vehicle service brakes. A total of 1,018 vehicles were involved in the study, with results indicating that variables such as vehicle production year, category, place of registration, engine power and displacement, gross and curb weight, and payload significantly affect the braking coefficient. Furthermore, the analysis revealed that factors such as vehicle production year, category, registration location, gross and curb weight, and payload are prominent in determining the braking force variability. Neural network analysis was employed to further assess these influential factors, highlighting that the year of manufacture, place of registration, and vehicle payload are particularly influential in predicting both compliance with minimum braking coefficient requirements and variations in braking force. The findings underscore the importance of these factors in the development of more precise models for vehicle brake performance, with potential implications for safety standards and regulatory frameworks.

This study analyzes the safety risk transmission mechanism in urban logistics drone last-mile delivery within the policy-driven low-altitude economy. To address the limitations of traditional risk identification methods, which rely heavily on accident data, this research integrates the Fuzzy Decision Analysis Laboratory Method (Fuzzy-DEMATEL),Interpretive Structural Modeling (ISM), and the Matrix of Cross-Impact Multiplication (MICMAC) to construct a three-dimensional analytical framework based on causal relationships, structural hierarchy, and attribute classification.First, Fuzzy-DEMATEL is employed to quantify the strength of causal relationships among risk factors. Next, ISM is used to deconstruct the multi-level hierarchical network and identify fundamental causes within the risk system. Finally, MICMAC is applied to calculate the dependencies and driving forces of each influencing factor, helping prioritize risk governance measures. The research findings indicate that: (1) The safety risk system of urban logistics drones for last-mile delivery exhibits a “dual-core driven – multi-loop coupled” characteristic. Equipment failures act as the physical carriers of systemic failures, while the root-cause risks stem from institutional factors such as inadequate pre-service training and violations of laws and regulations. (2) The risk hierarchy follows a pyramid-shaped transmission path, with risks propagating from the root layer to the surface in successive layers. Open airspace serves as an accelerator, transforming environmental disturbances into institutional defects, which in turn lead to technical failures. (3) The dependency attributes of each factor indicate the priority order for risk prevention and control: management leverage points serve as the strategic control core, the environment-technology interaction network is central to joint prevention, standardized processes solidify basic operations, and systemic risk levels are reduced.