Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction

TL;DR

This paper introduces a novel deep learning model combining parallel multi-graph convolution and bidirectional GRU to improve metro passenger volume prediction by capturing complex spatial and temporal dependencies.

Contribution

It proposes a new model, PB-GRU, that effectively utilizes multiple spatial correlation patterns and bidirectional temporal features for better accuracy.

Findings

PB-GRU achieves significantly lower prediction error than existing methods.

The model effectively captures origin-destination and flow pattern correlations.

Experiments on real-world datasets validate the model's superior performance.

Abstract

Accurate prediction of metro passenger volume (number of passengers) is valuable to realize real-time metro system management, which is a pivotal yet challenging task in intelligent transportation. Due to the complex spatial correlation and temporal variation of urban subway ridership behavior, deep learning has been widely used to capture non-linear spatial-temporal dependencies. Unfortunately, the current deep learning methods only adopt graph convolutional network as a component to model spatial relationship, without making full use of the different spatial correlation patterns between stations. In order to further improve the accuracy of metro passenger volume prediction, a deep learning model composed of Parallel multi-graph convolution and stacked Bidirectional unidirectional Gated Recurrent Unit (PB-GRU) was proposed in this paper. The parallel multi-graph convolution captures the origin-destination (OD) distribution and similar flow pattern between the metro stations, while bidirectional gated recurrent unit considers the passenger volume sequence in forward and backward directions and learns complex temporal features. Extensive experiments on two real-world datasets of subway passenger flow show the efficacy of the model. Surprisingly, compared with the existing methods, PB-GRU achieves much lower prediction error.