CSTP: Data-Efficient Road-Closure Prediction with Structured Inventory Dynamics

Road-closure planning requires rapid predictions, even though comparable disruptions are relatively rare in real-world data. We present the Closure-aware Structured Traffic Predictor (CSTP), which predicts vehicle inventory on road segments using a road-network graph, a closure query, and a corresponding normal-traffic scenario. CSTP represents departures from normal traffic through a compact physical state. Queue storage captures vehicles retained on each segment, while learned estimates of congestion-wave propagation, inventory depletion, and junction interactions determine how congestion develops over time and across the network. We learn road profiles and wave behavior from normal traffic. Separate closure scenarios calibrate only two response multipliers. Vehicle counts are computed from the resulting state, while learned prediction heads estimate speed and flow. We evaluate individual mechanisms using matched simulations and conduct grouped SUMO experiments in which test locations are excluded from parameter fitting and model selection. Relative to the Cell Transmission Model (CTM), CSTP reduces the mean absolute error of local vehicle counts by 28.8% and regional count error by 9.6%, with improvements in every tested local road group. Count error increases only slightly as the number of closure labels decreases, and CSTP retains the lowest core count error across the tested label budgets. PeMS incident records provide an additional real-world evaluation of speed and flow prediction. Repeated CPU tests show that CSTP is 5.2$\times$ faster at inference and uses 49% less peak memory than CTM combined with learned prediction heads.