MOHP-EC: A Multiobjective Hierarchical Prediction Framework for Urban Rail Transit Passenger Flow

Abstract

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-derivative control to compensate for the initial predicted value of every layer. As a final step, a trainable HP model is constructed based on deep learning to coordinate the prediction values of each layer. As examples, we construct three scenarios of passenger flow hierarchy based on the URT system in Wuxi, China. The constructed prediction framework is used to conduct experimental analyses. As a result, the MOHP-EC prediction framework could satisfy the hierarchical constraints and use the passenger flow hierarchical information to reduce prediction errors. The mean absolute error was reduced by 35%, and the root-mean-square error was reduced by 39%.