PKET-GCN: Prior knowledge enhanced time-varying graph convolution network for traffic flow prediction

Bao, Yinxin; Liu, Jiali; Shen, Qin-Qin; Cao, Yang; Ding, Weiping; Shi, Qingzhi

doi:10.1016/j.ins.2023.03.093

Cited by 15 publications

(2 citation statements)

References 42 publications

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“…CNN can effectively capture spatial features in traffic data through convolution operations; however, traditional CNN is designed based on image data with Euclidean structure and cannot be directly applied to deal with spatial dependencies in traffic data. To solve this problem, the GCN [30,31] has been proposed to deal with data with non-Euclidean structures, such as traffic networks. The GCN can perform convolution operations on the graph structure to extract the feature representation of nodes by aggregating their neighbor information.…”

Section: Traffic Speed Prediction With Deep Learningmentioning

confidence: 99%

Dynamic Spatiotemporal Correlation Graph Convolutional Network for Traffic Speed Prediction

Cao,

Bao,

Shi

et al. 2024

Symmetry

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Accurate and real-time traffic speed prediction remains challenging due to the irregularity and asymmetry of real-traffic road networks. Existing models based on graph convolutional networks commonly use multi-layer graph convolution to extract an undirected static adjacency matrix to map the correlation of nodes, which ignores the dynamic symmetry change of correlation over time and faces the challenge of oversmoothing during training iterations, making it difficult to learn the spatial structure and temporal trend of the traffic network. To overcome the above challenges, we propose a novel multi-head self-attention gated spatiotemporal graph convolutional network (MSGSGCN) for traffic speed prediction. The MSGSGCN model mainly consists of the Node Correlation Estimator (NCE) module, the Time Residual Learner (TRL) module, and the Gated Graph Convolutional Fusion (GGCF) module. Specifically, the NCE module aims to capture the dynamic spatiotemporal correlations between nodes. The TRL module utilizes a residual structure to learn the long-term temporal features of traffic data. The GGCF module relies on adaptive diffusion graph convolution and gated recurrent units to learn the key spatial features of traffic data. Experimental analysis on a pair of real-world datasets indicates that the proposed MSGSGCN model enhances prediction accuracy by more than 4% when contrasted with state-of-the-art models.

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Section: Traffic Speed Prediction With Deep Learningmentioning

confidence: 99%

Dynamic Spatiotemporal Correlation Graph Convolutional Network for Traffic Speed Prediction

Cao,

Bao,

Shi

et al. 2024

Symmetry

Self Cite

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“…Therefore, the operation of ITS is heavily dependent on precise traffic prediction, the core of which is modeling spatial-temporal dynamics of traffic features. Recent years have witnessed a widespread application of graph convolutional network (GCN) for extracting spatial correlations, where the distribution of traffic sensors is modeled as a series of nodes and edges in a graph ( Bao et al, 2023 ; Kong et al, 2022 ; Chen et al, 2022 ; Huang et al, 2022 ). In addition, recurrent neural network (RNN) and its variants, also known as long short-term memory (LSTM) and gated recurrent unit (GRU) have been extensively applied to model temporal dependency due to their outstanding performance in processing time series ( Zhao et al, 2023 ; Ma et al, 2023 ; Afrin & Yodo, 2022 ; Ma, Dai & Zhou, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Dynamic multiple-graph spatial-temporal synchronous aggregation framework for traffic prediction in intelligent transportation systems

Yu,

Bao,

Shi

2024

PeerJ Computer Science

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Accurate traffic prediction contributes significantly to the success of intelligent transportation systems (ITS), which enables ITS to rationally deploy road resources and enhance the utilization efficiency of road networks. Improvements in prediction performance are evident by utilizing synchronized rather than stepwise components to model spatial-temporal correlations. Some existing studies have designed graph structures containing spatial and temporal attributes to achieve spatial-temporal synchronous learning. However, two challenges remain due to the intricate dynamics: (a) Accounting for the impact of external factors in spatial-temporal synchronous modeling. (b) Multiple perspectives in constructing spatial-temporal synchronous graphs. To address the mentioned limitations, a novel model named dynamic multiple-graph spatial-temporal synchronous aggregation framework (DMSTSAF) for traffic prediction is proposed. Specifically, DMSTSAF utilizes a feature augmentation module (FAM) to adaptively incorporate traffic data with external factors and generate fused features as inputs to subsequent modules. Moreover, DMSTSAF introduces diverse spatial and temporal graphs according to different spatial-temporal relationships. Based on this, two types of spatial-temporal synchronous graphs and the corresponding synchronous aggregation modules are designed to simultaneously extract hidden features from various aspects. Extensive experiments constructed on four real-world datasets indicate that our model improves by 3.68–8.54% compared to the state-of-the-art baseline.

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Bidirectional Multi-grain Graph Convolution Network for Origin-Destination Demand Prediction

Liu,

Zhang,

Bian

et al. 2024

Communications in Computer and Information Science

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PKET-GCN: Prior knowledge enhanced time-varying graph convolution network for traffic flow prediction

Cited by 15 publications

References 42 publications

Dynamic Spatiotemporal Correlation Graph Convolutional Network for Traffic Speed Prediction

Dynamic Spatiotemporal Correlation Graph Convolutional Network for Traffic Speed Prediction

Dynamic multiple-graph spatial-temporal synchronous aggregation framework for traffic prediction in intelligent transportation systems

Bidirectional Multi-grain Graph Convolution Network for Origin-Destination Demand Prediction

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