Multiple dynamic graph based traffic speed prediction method

Zhang, Zikai; Li, Yidong; Song, Haifeng; Dong, Hairong

doi:10.1016/j.neucom.2021.07.052

Cited by 50 publications

(17 citation statements)

References 29 publications

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“…(2) ARIMA [16]: The basic idea of ARIMA is to treat the time-series data as a random series and describe this series approximately with a certain mathematical model. A smoothness test is performed on the data first, and an ARMA model is fitted for prediction.…”

Section: B Baselinesmentioning

confidence: 99%

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A Hybrid Model Integrating Local and Global Spatial Correlation for Traffic Prediction

et al. 2022

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Accurate traffic prediction can effectively alleviate traffic congestion problems. The complex spatial correlation of traffic flow contributes to the challenging prediction problem. Most current prediction methods focus on learning local spatial correlation, ignoring the spatial correlation of long-distance traffic flow. In this paper, we combine the improved Graph Convolutional Network (GCN) with Gated Recurrent Unit (GRU) to propose a hybrid model integrating local and global spatial correlation (T-LGGCN) for traffic prediction. The model consists of two parts: global spatial-temporal component and local spatial-temporal component. For the global spatial-temporal component, we construct the global correlation matrix to improve the GCN for obtaining the global spatial correlation. And GRU is stacked to obtain the global spatial-temporal correlation. For the local spatial-temporal component, we utilize the strategy of combining Fully Connected Layer (FCL) and GCN to analyze the local spatial correlation. Similarly, GRU is used to perform the output of local spatial-temporal correlation. The output of the two components is finally summed, and the prediction results are generated with the dense network. Experiments were conducted using the highway datasets PEMS04 and PEMS08 from the Caltrans Performance Measurement System, and the results show that our model significantly outperforms state-of-the-art baselines.

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Section: B Baselinesmentioning

confidence: 99%

“…(2) GCN shows good prediction performance on the non-Euclidean datasets [15]. By aggregating the spatial information through the topology of the road network, it can perform a better analysis of the spatial correlation of traffic flow [16][17]. However, it is already a well-known problem that multilayer GCNs can cause smoothing [18].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Integrating Local and Global Spatial Correlation for Traffic Prediction

et al. 2022

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“…The key component of a GNN is the adjacency matrix, which describes the graph structure and preserves the spatial correlations between nodes. For traffic speed forecasting, most works ( 6 – 13 ) usually construct the adjacency matrices based on the Euclidean distance between sensors in the traffic network. But Chen et al argued that the graph structure constructed in this way neglects the complexity and interaction of edges ( 9 ).…”

Section: Related Workmentioning

confidence: 99%

Multiadaptive Spatiotemporal Flow Graph Neural Network for Traffic Speed Forecasting

Liu

Mao

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Traffic speed forecasting plays an important role in intelligent traffic monitoring systems. Existing methods mostly predefine a fixed adjacency matrix to capture the spatial correlation between sensors in a traffic network. However, there are multiple hidden spatial correlations between sensors. A single fixed adjacency matrix cannot adaptively capture multiple spatial correlations. To overcome this limitation, we proposed a novel multiadaptive spatiotemporal flow graph neural network (MAF-GNN) for traffic speed forecasting. Specifically, MAF-GNN mainly consists of a multiadaptive adjacency matrix mechanism and a spatiotemporal flow mechanism. The multiadaptive adjacency matrix mechanism was proposed to adaptively capture multiple hidden spatial correlations between sensors. The spatiotemporal flow mechanism was proposed to further enhance the capture of temporal and spatial correlations. The experimental results on two real-world traffic datasets, METR-LA and PeMS-Bay, demonstrated the superiority of MAF-GNN. MAF-GNN outperformed baseline models in 1-h ahead forecasting.

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“…On the other hand, prefractal graphs are also dynamic graphs [26][27][28]. Large prefractal graphs are used to build graph models with the help of which optimization problems are solved [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Introduction to the Class of Prefractal Graphs

Kochkarov

2022

Mathematics

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Fractals are already firmly rooted in modern science. Research continues on the fractal properties of objects in physics, chemistry, biology and many other scientific fields. Fractal graphs as a discrete representation are used to model and describe the structure of various objects and processes, both natural and artificial. The paper proposes an introduction to prefractal graphs. The main definitions and notation are proposed—the concept of a seed, the operations of processing a seed, the procedure for generating a prefractal graph. Canonical (typical) and non-canonical (special) types of prefractal graphs are considered separately. Important characteristics are proposed and described—the preservation of adjacency of edges for different ranks in the trajectory. The definition of subgraph-seeds of different ranks is given separately. Rules for weighting a prefractal graph by natural numbers and intervals are proposed. Separately, the definition of a fractal graph as infinite is given, and the differences between the concepts of fractal and prefractal graphs are described. At the end of the work, already published works of the authors are proposed, indicating the main backlogs, as well as a list of directions for new research. This work is the beginning of a cycle of works on the study of the properties and characteristics of fractal and prefractal graphs.

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Multiple dynamic graph based traffic speed prediction method

Cited by 50 publications

References 29 publications

A Hybrid Model Integrating Local and Global Spatial Correlation for Traffic Prediction

A Hybrid Model Integrating Local and Global Spatial Correlation for Traffic Prediction

Multiadaptive Spatiotemporal Flow Graph Neural Network for Traffic Speed Forecasting

Introduction to the Class of Prefractal Graphs

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