Efficient real-time scheduling of bus operations remains a critical challenge in urban transit systems, particularly under uncertain traffic conditions and high passenger demand variability. Our existing Double Deep Q-Network (Double DQN) implementation has demonstrated promising results in learning optimal holding and stop-skipping policies for bus operations. However, as urban networks grow in complexity and require […]
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