Abstract
Predicting the passenger flow of metro networks is of great importance for traffic management and public safety. However, such predictions are very challenging, as passenger flow is affected by complex spatial dependencies (nearby and distant) and temporal dependencies (recent and periodic). In this paper, we propose a novel deep-learning-based approach, named STGCNNmetro (spatiotemporal graph convolutional neural networks for metro), to collectively predict two types of passenger flow volumes—inflow and outflow—in each metro station of a city. Specifically, instead of representing metro stations by grids and employing conventional convolutional neural networks (CNNs) to capture spatiotemporal dependencies, STGCNNmetro transforms the city metro network to a graph and makes predictions using graph convolutional neural networks (GCNNs). First, we apply stereogram graph convolution operations to seamlessly capture the irregular spatiotemporal dependencies along the metro network. Second, a deep structure composed of GCNNs is constructed to capture the distant spatiotemporal dependencies at the citywide level. Finally, we integrate three temporal patterns (recent, daily, and weekly) and fuse the spatiotemporal dependencies captured from these patterns to form the final prediction values. The STGCNNmetro model is an end-to-end framework which can accept raw passenger flow-volume data, automatically capture the effective features of the citywide metro network, and output predictions. We test this model by predicting the short-term passenger flow volume in the citywide metro network of Shanghai, China. Experiments show that the STGCNNmetro model outperforms seven well-known baseline models (LSVR, PCA-kNN, NMF-kNN, Bayesian, MLR, M-CNN, and LSTM). We additionally explore the sensitivity of the model to its parameters and discuss the distribution of prediction errors.
Highlights
The prediction of short-term passenger flow volume is a vital component of metro systems
This paper introduces Graph Convolutional Neural Networks (GCNNs) to predict the station-level short-term passenger flow volume in a citywide metro network, proposing a novel deep learning (DL) method named STGCNNmetro
The following can be seen from Table 2: (1) Compared with the other seven models, the root-mean-square error (RMSE) of the STGCNNmetro model is the smallest, and the mean absolute percentage error (MAPE) of the principal component analysis (PCA)-k-nearest neighbor (kNN) model is the smallest
Summary
The prediction of short-term passenger flow volume is a vital component of metro systems. Many passenger-flow prediction models have been proposed based on statistical and machine learning (ML) algorithms, such as support vector machine (SVM) [2,3,4], Bayesian regression [5], principal component analysis (PCA) [6,7], non-negative matrix factorization (NMF) [8], and artificial neural networks (ANNs) [1,9,10,11] These conventional methods cannot process raw sample data and require a manual feature engineering procedure. This paper introduces GCNNs to predict the station-level short-term passenger flow volume in a citywide metro network, proposing a novel DL method named STGCNNmetro (spatiotemporal graph convolutional neural networks for metro). The main contribution of this paper is that it proposes a novel “end-to-end” DL-based model which is able to automatically capture the irregular spatiotemporal dependencies of a metro network from raw passenger flow-volume data and achieve accurate predictions of passenger flow volume for the citywide metro system
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