We consider the centralized traffic routing (CTR) problem, where a central planner assigns routes to origin-destination (OD) vehicle flows to minimize congestion-dependent segment cost. Prior work formulates CTR efficiently as a mixed-integer linear program (MILP), but scaling this formulation to the full road network is computationally prohibitive within an operational time budget. Bounded Routes and Smart Routes speed up optimization using precomputed reduced networks; however, these methods do not account for vehicle information within each OD flow. Motivated by this gap, we propose a learning-based urban network reduction approach. A dedicated graph neural network (GNN) first scores road segments for each OD flow using the road network and vehicle-flow information. Highly ranked segments are then incorporated into complete OD routes, and the original segment-flow MILP is solved on the resulting reduced road network. The GNN thus guides the reduction of the road network, while the solver enforces feasibility and determines the routing. Experiments on four real urban networks -- Prague-Dejvice, London, New York, and Sydney -- show that the proposed approach achieves the smallest relative objective difference to the $30$-second full-graph reference in every city under both traffic regimes. Averaged across cities, this difference is more than six times lower than that of the best competing method. Moreover, traffic simulation results show that the optimization gains of the proposed method lead to more efficient routing, with the highest aggregate arrival rate and a competitive trip duration per arrival percentage point.

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