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For sophisticated management, advertisement placement, and epidemic prevention control of urban rail transit (URT), accurate and real-time predictions of passenger flows at different levels are of great importance. Unlike traditional prediction tasks, hierarchical prediction (HP) requires that the hierarchical constraints be satisfied as much as possible (the sum of the predicted passenger flow of child nodes should nearly equal the parent node) to achieve realistic predictions. This article proposes a multiobjective HP (MOHP) framework with an error compensation (EC) mechanism for predicting URT passenger flow with a hierarchical structure. Three components are included: the initial prediction module, the EC module, and the hierarchical coordination module. In the initial prediction module, the initial passenger flow prediction of each layer is carried out. The EC model is developed based on proportional-integral …