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Accurate prediction of metro passenger volume (number of passengers) isvaluable to realize real-time metro system management, which is a pivotal yetchallenging task in intelligent transportation. Due to the complex spatialcorrelation and temporal variation of urban subway ridership behavior, deeplearning has been widely used to capture non-linear spatial-temporaldependencies. Unfortunately, the current deep learning methods only adopt graphconvolutional network as a component to model spatial relationship, withoutmaking 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 stackedBidirectional unidirectional Gated Recurrent Unit (PB-GRU) was proposed in thispaper. The parallel multi-graph convolution captures the origin-destination(OD) distribution and similar flow pattern between the metro stations, whilebidirectional gated recurrent unit considers the passenger volume sequence inforward and backward directions and learns complex temporal features. Extensiveexperiments on two real-world datasets of subway passenger flow show theefficacy of the model. Surprisingly, compared with the existing methods, PB-GRUachieves much lower prediction error.

Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction