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This paper presents a preference-conditioned multi-objective reinforcement learning controller for transit signal priority that allows runtime tuning of the trade-off between bus priority and overall traffic delay without retraining. Experiments show it outperforms fixed-time and rule-based baselines while maintaining feasibility constraints.