Metro passenger flow prediction is a strategically necessary demand in an intelligent transportation system to alleviate traffic pressure, coordinate operation schedules, and plan future constructions. Graph-based neural networks have been widely used in traffic flow prediction problems. Graph Convolutional Neural Networks (GCN) captures spatial features according to established connections but ignores the high-order relationships between stations and the travel patterns of passengers. In this paper, we utilize a novel representation to tackle this issue - hypergraph. A dynamic spatio-temporal hypergraph neural network to forecast passenger flow is proposed. In the prediction framework, the primary hypergraph is constructed from metro system topology and then extended with advanced hyperedges discovered from pedestrian travel patterns of multiple time spans. Furthermore, hypergraph convolution and spatio-temporal blocks are proposed to extract spatial and temporal features to achieve node-level prediction. Experiments on historical datasets of Beijing and Hangzhou validate the effectiveness of the proposed method, and superior performance of prediction accuracy is achieved compared with the state-of-the-arts.

Graph convolutional networks (GCN) have been applied in the traffic flow forecasting tasks with the graph capability in describing the irregular topology structures of road networks. However, GCN based traffic flow forecasting methods often fail to simultaneously capture the short-term and long-term temporal relations carried by the traffic flow data, and also suffer the over-smoothing problem. To overcome the problems, we propose a hierarchical traffic flow forecasting network by merging newly designed the long-term temporal Transformer network (LTT) and the spatio-temporal graph convolutional networks (STGC). Specifically, LTT aims to learn the long-term temporal relations among the traffic flow data, while the STGC module aims to capture the short-term temporal relations and spatial relations among the traffic flow data, respectively, via cascading between the one-dimensional convolution and the graph convolution. In addition, an attention fusion mechanism is proposed to combine the long-term with the short-term temporal relations as the input of the graph convolution layer in STGC, in order to mitigate the over-smoothing problem of GCN. Experimental results on three public traffic flow datasets prove the effectiveness and robustness of the proposed method.

Data missing remains a difficult and important problem in the transportation information system, which seriously restricts the application of the intelligent transportation system (ITS), dominatingly on traffic monitoring, e.g., traffic data collection, traffic state estimation, and traffic control. Numerous traffic data imputation methods had been proposed in the last decade. However, lacking of sufficient temporal variation characteristic analysis as well as spatial correlation measurements leads to limited completion precision, and poses a major challenge for an ITS. Leveraging the low-rank nature and the spatial-temporal correlation of traffic network data, this paper proposes a novel approach to reconstruct the missing traffic data based on low-rank matrix factorization, which elaborates the potential implications of the traffic matrix by decomposed factor matrices. To further exploit the temporal evolvement characteristics and the spatial similarity of road links, we design a time-series constraint and an adaptive Laplacian regularization spatial constraint to explore the local relationship with road links. The experimental results on six real-world traffic data sets show that our approach outperforms the other methods and can successfully reconstruct the road traffic data precisely for various structural loss modes.

Traffic prediction methods on a single-source data have achieved excellent results in recent years, especially the Graph Convolutional Networks (GCN) based models with spatio-temporal dependency. In reality, various modes of urban transportation operate simultaneously. They influence and complement each other in common space-time occasions, constituting the transportation system dynamically. Thus, traffic data from multiple sources is ostensibly heterogeneous, but internally correlated. The typical single data driven models are, however, not universally applicable for heterogeneous traffic data. To address this issue, we propose a Multi-task Hypergraph Convolutional Neural Network (MT-HGCN) for the multi-source traffic prediction problem. The framework consists of a main task and a related task. Both tasks are based on Hypergraph Convolutional Neural Networks (HGCN) and are devoted to two prediction problems. Furthermore, the tasks are bridged by a feature compress unit, which models the correlation and shares the latent feature to improve the performance of the main task. The node-level forecasting has been evaluated on historical datasets of Beijing to verify the effectiveness of the proposed method. Compared with the state-of-the-arts, the superior performance of the proposed method can be obtained.

The traffic data corrupted by noise and missing entries often lead to the poor performance of Intelligent Transportation Systems (ITS), such as the bad congestion prediction and route guidance. How to efficiently impute the traffic data is an urgent problem. As a classic deep learning method, Generative Adversarial Network (GAN) achieves remarkable success in image recovery fields, which opens up a new way for the traffic data imputation. In this paper, we propose a novel spatio-temporal GAN model for the traffic data imputation (STGAN). Firstly, we design the generative loss and center loss, which not only minimizes the reconstructed errors of the imputed entries, but also ensures each imputed entry and its neighbors conform to the local spatio-temporal distribution. Then, the discriminator uses the convolution neural network classifier to judge whether the imputed matrix conforms to the global spatio-temporal distribution. As for the network architecture of the generator, we introduce the skip-connection to keep all well preserved data unchanged, and employ the dilated convolution to capture the spatio-temporal correlation in the traffic data. The experimental results show that our proposed method obviously outperforms other competitive traffic data imputation methods.

With the powerful learning ability of deep convolutional networks, deep clustering methods can extract the most discriminative information from individual data and produce more satisfactory clustering results. However, existing deep clustering methods usually ignore the relationship between the data. Fortunately, the graph convolutional network can handle such relationships, opening a new research direction for deep clustering. In this paper, we propose a cross-attention based deep clustering framework, named Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN), which contains four main modules: the cross-attention fusion module which innovatively concatenates the Content Auto-encoder module (CAE) relating to the individual data and Graph Convolutional Auto-encoder module (GAE) relating to the relationship between the data in a layer-by-layer manner, and the self-supervised model that highlights the discriminative information for clustering tasks. While the cross-attention fusion module fuses two kinds of heterogeneous representation, the CAE module supplements the content information for the GAE module, which avoids the over-smoothing problem of GCN. In the GAE module, two novel loss functions are proposed that reconstruct the content and relationship between the data, respectively. Finally, the self-supervised module constrains the distributions of the middle layer representations of CAE and GAE to be consistent. Experimental results on different types of datasets prove the superiority and robustness of the proposed CaEGCN.