SST-GNN: Simplified Spatio-Temporal Traffic Forecasting Model Using Graph Neural Network

Roy, A. K.; Roy, Kashob Kumar; Ali, Amin Ahsan; Amin, M. Ashraful; Rahman, A K M Mahbubur

doi:10.1007/978-3-030-75768-7_8

Cited by 20 publications

(9 citation statements)

References 8 publications

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“…The GCN-based approaches above mainly use the recent data to represent temporal information while ignoring the periodicity in traffic flow. Roy et al [14] proposed the Simplified Spatio-temporal Traffic GNN (SST-GNN) to capture the periodic traffic patterns by adopting a novel position encoding scheme. Chen et al proposed the Temporal Directed GCN (T-DGCN) [15] which utilizes a novel global position encoding strategy to capture temporal dependence such as daily periodicity.…”

Section: Traffic Flow Forecastingmentioning

confidence: 99%

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Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting

Zhang

Qiu

Jian³

et al. 2022

IJGI

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Due to the periodic and dynamic changes of traffic flow and the spatial–temporal coupling interaction of complex road networks, traffic flow forecasting is highly challenging and rarely yields satisfactory prediction results. In this paper, we propose a novel methodology named the Augmented Multi-component Recurrent Graph Convolutional Network (AM-RGCN) for traffic flow forecasting by addressing the problems above. We first introduce the augmented multi-component module to the traffic forecasting model to tackle the problem of periodic temporal shift emerging in traffic series. Then, we propose an encoder–decoder architecture for spatial–temporal prediction. Specifically, we propose the Temporal Correlation Learner (TCL) which incorporates one-dimensional convolution into LSTM to utilize the intrinsic temporal characteristics of traffic flow. Moreover, we combine TCL with the graph convolutional network to handle the spatial–temporal coupling interaction of the road network. Similarly, the decoder consists of TCL and convolutional neural networks to obtain high-dimensional representations from multi-step predictions based on spatial–temporal sequences. Extensive experiments on two real-world road traffic datasets, PEMSD4 and PEMSD8, demonstrate that our AM-RGCN achieves the best results.

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Section: Traffic Flow Forecastingmentioning

confidence: 99%

“…For the former, most existing approaches [12][13][14][15][16][17] only paid attention to the periodicity of the traffic flow regardless of periodic temporal shift, which resulted in the non-comprehensive capture of temporal characteristics. Thus, the robustness and accuracy achieved by these models cannot meet expectations.…”

Section: Introductionmentioning

confidence: 99%

Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting

Zhang

Qiu

Jian³

et al. 2022

IJGI

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show abstract

“…Moreover, theory and research have shown that it is difficult for RNNs to learn to store very long time series [18][19][20]. ASTGCN [13] and Diffusion Convolutional Recurrent Neural Network (DCRNN) [21] use an iterative prediction mechanism in which all predicted values for multiple time steps are obtained by a single uniform evaluation rather than various iterations.…”

Section: Introductionmentioning

confidence: 99%

STSGAN: Spatial-Temporal Global Semantic Graph Attention Convolution Networks for Urban Flow Prediction

Zhou

Qin

et al. 2022

IJGI

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Accurate urban traffic flow prediction plays a vital role in Intelligent Transportation System (ITS). The complex long-term and long-range spatiotemporal correlations of traffic flow pose a significant challenge to the prediction task. Most current research methods focus only on spatial correlations in local areas, ignoring global geographic contextual information. It is challenging to capture spatial information from distant nodes using shallow graph neural networks (GNNs) to model long-range spatial correlations. To handle this problem, we design a novel spatiotemporal global semantic graph-attentive convolutional network model (STSGAN), which is a deep-level network to achieve the simultaneous modelling of spatiotemporal correlations. First, we propose a graph-attentive convolutional network (GACN) to extract the importance of different spatial features and learn the spatial correlation of local regions and the global spatial semantic information. The temporal causal convolution structure (TCN) is utilized to capture the causal relationships between long-short times, thus enabling an integrated consideration of local and overall spatiotemporal correlations. Several experiments are conducted on two real-world traffic flow datasets, and the results show that our approach outperforms several state-of-the-art baselines.

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“…Graph neural networks (GNNs) are deep learning-based methods that operate on graphs or networks where other types of machine-learning methods such as convolutional neural networks (CNNs) or recurrent neural network (RNNs) cannot be implemented because of the irregular and non-Euclidean nature of the complex network. GNN has become a widely used method for network analysis because of its convincing performance in various fields, such as estimation of molecular properties [30,31], drug discovery [32], and traffic forecasting [33,34]. In the epidemic field, GNNs have been employed for the prediction of disease prevalence [35][36][37], identification of patient zero [38], and estimation of epidemic state using limited information [39].…”

Section: Introductionmentioning

confidence: 99%

Effective vaccination strategy using graph neural network ansatz

Jhun¹

2021

Preprint

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SST-GNN: Simplified Spatio-Temporal Traffic Forecasting Model Using Graph Neural Network

Cited by 20 publications

References 8 publications

Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting

Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting

STSGAN: Spatial-Temporal Global Semantic Graph Attention Convolution Networks for Urban Flow Prediction

Effective vaccination strategy using graph neural network ansatz

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