A Graph Neural Network Surrogate Model for Incident-Based Travel Time Prediction Under Limited Sensor Data

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Localized roadway incidents can produce congestion well beyond their point of origin, but evaluating these effects with microscopic simulation requires a separate run for each scenario. This paper presents a Spatio-Temporal Graph Convolutional Network (STGCN) for forecasting route-level travel times on a simulated Nashville, Tennessee road network with 1,037 junctions and 1,601 road segments. The model uses directional traffic counts from only 129 signalized intersections, reflecting data commonly available to transportation agencies. By representing these intersections as a graph, the STGCN jointly captures spatial dependence and temporal traffic evolution to learn how localized disruptions propagate through the network. We compare a baseline trained on 80 incident-free simulations with an incident-inclusive model trained with an additional 360 lane-blockage scenarios across 12 locations and three durations. The baseline achieved a relative mean absolute error (MAE) of 9.94%. The incident-inclusive model achieved 9.67% overall, 6.45% during active incidents, and 13.62% at 30 incident locations withheld entirely from training. On disrupted routes, its predictions were within 2.0 minutes of observed travel time on average. For comparison, simulated travel times vary by 7.4%, or 1.3 minutes, across random seeds under identical conditions. Inference requires approximately 53 ms on a single CPU core, demonstrating the potential of the model as an efficient surrogate for transportation resilience screening, incident management, and proactive rerouting.

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