Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction

Zhao, Chuan; Li, Xin; Shao, Zezhi; Yang, Hongji; Wang, Fei

doi:10.1080/09540091.2022.2061915

Cited by 10 publications

(3 citation statements)

References 40 publications

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“…In some studies, only one aspect is taken into consideration when making the adjacency matrix [75], [82], [93], while in others, different aspects are combined in a weighted manner [61], [85], [93], [212]. Some others also use the multi-graph concept instead of combining the multiple adjacency matrices into one matrix [49], [63], [73], [113]. In this subsection, we provide a list of different approaches for defining adjacency matrices in various studies:…”

Section: A Graph Construction In Graph Neural Networkmentioning

confidence: 99%

“…There are several types of connectivity, which are either caused by roads such as motorways and highways, or by public transportation, such as subways. A connectivity matrix, also called "physical matrix" [85], is a kind of adjacency matrix that has been used in several studies to demonstrate such associations between nodes [86], [112], [113].…”

Section: A Graph Construction In Graph Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domainspecific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNNbased frameworks.

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Section: A Graph Construction In Graph Neural Networkmentioning

confidence: 99%

Section: A Graph Construction In Graph Neural Networkmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Hu et al, 2021), knowledge tracing (Song et al, 2022(Song et al, , 2021Y. Yang et al, 2021), traffic prediction (N. Zhao et al, 2022), and environmental monitoring (Chang et al, 2021). In addition, GCN is also widely used in computer vision (Tan et al, 2020), recommender systems (Wang et al, 2019), and natural language processing (Mishra et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Context-sensitive graph representation learning

Qin

Zeng

et al. 2023

Int. J. Mach. Learn. & Cyber.

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Graph Convolutional Network (GCN) is a powerful emerging deep learning technique for learning graph data. However, there are still some challenges for GCN. For example, the model is shallow; the performance is poor when labelled nodes are severely scarce. In this paper, we propose a Multi-Semantic Aligned Graph Convolutional Network (MSAGCN), which contains two fundamental operations: multi-angle aggregation and semantic alignment, to resolve two challenges simultaneously. The core of MSAGCN is the aggregation of nodes that belong to the same class from three perspectives: nodes, features, and graph structure, and expects the obtained node features to be mapped nearby. Specifically, multi-angle aggregation is applied to extract features from three angles of the labelled nodes, and semantic alignment is utilised to align the semantics in the extracted features to enhance the similar content from different angles. In this way, the problem of over-smoothing and over-fitting for GCN can be alleviated. We perform the node clustering task on three citation datasets, and the experimental results demonstrate that our method outperforms the state-of-the-art (SOTA) baselines.

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