A Dynamic Bayesian Network (DBN) consists of multiple time-slice BNs that can describe changes in resilience over time [20; 21]. The DBN network structure often takes the stage state or functional elements of resilience as dynamic nodes. The relationship between nodes is constructed based on the evolutionary law of resilience in the field. [PERSON] et al. considered the possibility of industrial facility systems maintaining or restoring their normal functions during and after interruptions. They constructed a Markov chain model for system absorption, adaptation, recovery, and learning state transitions, which was then converted into DBN [22]. [PERSON] et al. used robustness, vulnerability, resourcefulness, and agility as four key resilience elements to construct a DBN for housing infrastructure against flood disasters [23]. [PERSON] et al. used the functional resonance analysis method (FRAM) to establish a network structure model of accident evolution. They constructed DBN to depict the interaction between accidents and emergency measures [24]. DBN parameter learning (including unconditional and conditional probability) is the key to resilience quantification based on DBN. Conditional probability refers to the probability that a specific state of a child node occurs under the known state of a parent node. In resilience quantification, this state usually refers to whether resilience is good or not. Conditional probability is closely related to the dependency relationship between nodes and the probability distribution of node resilience status. However, it is not easy to directly obtain data for judging node resilience status, so making judgments on network node resilience status is a prerequisite and key for DBN parameter learning. The resilience status of nodes can be determined by combining expert knowledge with actual data [25; 26]. [PERSON] evaluated resilience status based on expert judgment and triangular fuzzy function (TFN) [27]. However, TFN cannot conduct probability transmission, which indicates the failure to transfer the information of a fixed node to other nodes in the task of resilience deduction. [PERSON] used Boolean expressions to calculate the probability distribution of node resilience status [18]. [PERSON] simulated the impact of parent nodes on child node resilience status using the NoisyOR function [28]. Although the existing research has explored the methods of evaluating the alternation of resilience status, further study is required to fully consider the complex dependency influenced by multiple factors between nodes to judge node resilience status accurately. In addition, when multiple nodes contain information that conflicts with each other for judging resilience status, conflicting information will also be challenging to handle. For processing multi-source information, the Dempster-Shafer evidence theory provides a method of uncertain reasoning by calculating judgments' credibility by merging various kinds of evidence quantities [29]. Road resilience is affected by many factors, such as people, vehicles, roads, and the environment. In Bayesian networks, judging node resilience status can be regarded as a multi-criteria decision-making problem. The influence weight of multiple nodes can be determined by using the AHP hierarchical analysis method [30], the TOPSIS method [31], the VIKOR method [32], or the BWM method for the multi-criteria compromise solution ranking method. Among them, the BWM method is suitable for solving the problem of determining node influence weight due to its agility and reliability in the decision-making process [33]. Therefore, in the Dynamic Bayesian Network-based resilience quantification method, network structure learning should consider multiple factors and depict how resilient elements interact in the road operational process. In contrast, parameter learning should consider multiple factors' complex coupling effects and apply methods that fit uncertain data in road operational scenarios to judge network node states.