Resilience Assessment of Inland Waterway Shipping Logistics Corridors Under Complex Navigational Environments: A CA–DPSIR Framework and Structure-Constrained Dynamic Bayesian Inference

Abstract

Against the backdrop of normalized multiple disturbances and uncertainties, the resilience of inland waterway shipping logistics corridors is of critical importance for maintaining regional logistics stability. To address the insufficient representation of corridor system attributes and the limited characterization of dynamic feedback among different dimensions, this study integrates corridor attributes with the process logic of the Driving Force–Pressure–State–Impact–Response (DPSIR) framework to develop a Channel Attributes–DPSIR framework. Dynamic weighting and structure-constrained dynamic Bayesian inference are further incorporated to characterize the time-varying features, coupling relationships, and uncertainties of resilience. Using the continuous corridor in the lower reaches of the Jinsha River as a case study, an empirical analysis is conducted based on data from 2015 to 2024. The results indicate that the overall corridor resilience improved steadily; driving and response resilience were relatively high, whereas state resilience showed slow improvement, acting as the primary bottleneck. The posterior results indicate that response has a stable feedback effect on drivers, impact-related losses can stimulate response investment, and pressure exerts a limited negative regulatory effect on state.