World Model Application: How Discrete Structural Annealing and Correctable Structural Intelligence Assist in Solving Urban Traffic Congestion

Urban traffic congestion management has long been constrained by the structural limitations of the continuous calculus paradigm: partial differential equation-based traffic flow models struggle to capture the topological mutations induced by accidents, while deep learning predictors lack auditable causal paths and local correction mechanisms. This paper proposes a traffic world model architecture based on Discrete Structural Annealing (DSA) and Correctable Structural Intelligence (CSI). In this architecture, the road network is encoded as a discrete topological graph, accidents are modeled as node ruptures, and traffic recovery is achieved through local structural rewriting for topological self-healing. To validate the architecture, we designed a Proof-of-Concept microscopic simulation: 500 vehicles are deployed on a 10×10 grid network, where baseline travel times follow a normal distribution to simulate heterogeneity, and accident queuing delays follow an exponential distribution to characterize congestion long-tails. Results show that traditional static routing lacking topological awareness yields an average travel time of 52.39 s (SD 42.72) under sudden accidents, whereas the DSA+CSI architecture compresses this to 15.06 s (SD 3.32), improving efficiency by 71.2% and converging the distribution from an extreme long-tail to a sharp normal form. This result quantitatively demonstrates that discrete deduction driven by local structural rewriting can compress extreme system uncertainty by over an order of magnitude, providing an empirical basis for next-generation auditable traffic world models.