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Such networks exhibit strong spatial-temporal coupling, where link travel times evolve as stochastic processes over graph edges, making the problem inherently sequential under uncertainty. Existing stochastic on-time arrival (SOTA) methods primarily depend on the current node and remaining budget, which limits their ability to exploit trajectory-level temporal structure and history-dependent correlations. This work proposes GPG-HT, a history-aware graph-signal policy framework that integrates a Decision Transformer with generalized policy gradient optimization for reliable routing. 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