A dynamic graph deep learning model with multivariate empirical mode decomposition for network-wide metro passenger flow prediction

Abstract

Network-wide short-term passenger flow prediction is critical for the operation and management of metro systems. However, it is challenging due to the inherent non-stationarity, nonlinearity, and spatial–temporal dependencies within passenger flow. To tackle these challenges, this paper introduces a hybrid model called multi-scale dynamic propagation spatial–temporal network (MSDPSTN). Specifically, the model employs multivariate empirical mode decomposition to jointly decompose the multivariate passenger flow into multi-scale intrinsic mode functions. Then, a set of dynamic graphs is developed to reveal the passenger propagation law in metro networks. Based on the representation, a deep learning model is proposed to achieve multistep passenger flow prediction, which employs the dynamic propagation graph attention network with long short-term memory to extract the spatial–temporal dependencies. Extensive experiments conducted on a real-world dataset from Chengdu, China, validate the superiority of the proposed model. Compared to state-of-the-art baselines, MSDPSTN reduces the mean absolute error, root mean squared error, and mean absolute percentage error by at least 3.243%, 4.451%, and 4.139%, respectively. Further quantitative analyses confirm the effectiveness of the components in MSDPSTN. This paper contributes to addressing inherent features of passenger flow to enhance prediction performance, offering critical insights for decision-makers in implementing real-time operational strategies.