Short-Term Online Forecasting for Passenger Origin–Destination (OD) Flows of Urban Rail Transit: A Graph–Temporal Fused Deep Learning Method

Abstract

Predicting short-term passenger flow accurately is of great significance for daily management and for a timely emergency response of rail transit networks. In this paper, we propose an attention-based Graph–Temporal Fused Neural Network (GTFNN) that can make online predictions of origin–destination (OD) flows in a large-scale urban transit network. In order to solve the key issue of the passenger hysteresis in online flow forecasting, the proposed GTFNN takes finished OD flow and a series of features, which are known or observable, as the input and performs multi-step prediction. The model is constructed from capturing both spatial and temporal characteristics. For learning spatial characteristics, a multi-layer graph neural network is proposed based on hidden relationships in the rail transit network. Then, we embedded the graph convolution into a Gated Recurrent Unit to learn spatial–temporal features. For learning temporal characteristics, a sequence-to-sequence structure embedded with the attention mechanism is proposed to enhance its ability to capture both local and global dependencies. Experiments based on real-world data collected from Chongqing’s rail transit system show that the metrics of GTFNN are better than other methods, e.g., the SMAPE (Symmetric Mean Absolute Percentage Error) score is about 14.16%, with a range from 5% to 20% higher compared to other methods.

Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns

“…Early applications of parametric models often employed growth curves for forecasting metrics like rail transit passenger volumes (Yuan et al [12]). Common among these parametric approaches are various timeseries models and their derivatives, which are praised for their simplicity and interpretability [13][14][15]. Nonetheless, these models traditionally falter when addressing the non-linear nature of traffic flows, often leading to substantial prediction errors.…”

Impact of Traffic Flow Rate on the Accuracy of Short-Term Prediction of Origin-Destination Matrix in Urban Transportation Networks

“…An important direction in the development of methods to predict traffic volume or estimate the OD matrix is the use of deep learning [7,[13][14][15]24,[34][35][36][37][38]. The research results indicate that these methods significantly increase the possibilities and reliability of traffic prediction.…”