Spatial‐temporal correlation graph convolutional networks for traffic forecasting

Huang, Ru; Chen, Zijian; Zhai, Guangtao; He, Junjia; Chu, Xiaoli

doi:10.1049/itr2.12330

Cited by 4 publications

(6 citation statements)

References 52 publications

(107 reference statements)

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“…It should be noted that the experiments are based on historical data from the previous 60 min. That is, seq len takes the value 12 and predicts the traffic speed for the next (15,30,60) min, that is, pre len takes the values 3, 6, and 12, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Even though CNN is effective for capturing spatial features of traffic networks with a Euclidean architecture, it struggles to capture spatial features of traffic networks with a non-Euclidean architecture, which is the case for almost all traffic networks. To address this problem, a new approach, called GCN, 30,31,34,[39][40][41][42] has been introduced; it processes irregular data and captures spatial correlations efficiently. Zhao et al 18 benefited from the advantages of the GCN; they included a GRU and suggested a novel method, temporal GCN (T-GCN), for predicting the traffic status on urban roads.…”

Section: Spatial and Temporal Neural Networkmentioning

confidence: 99%

“…In summary, no model from previous works can efficiently handle all the mentioned challenges because most face difficulties in capturing spatial-temporal dependencies or long-term predictions. Therefore, researchers developed models that combine spatial and temporal neural networks with the attention technique to benefit from their advantages, 29,30,32,33,47,48 and they succeeded to a large extent. For example, Tang and Zeng 29 proposed a hybrid model called spatial-temporal correlation graph convolutional networks (STCGCN).…”

Section: Spatial-temporal Attention Networkmentioning

confidence: 99%

“…For this reason, researchers proposed models that combine different techniques to produce the best results. [29][30][31][32][33][34] Most of the above methods faced difficulties in handling long-term predictions. While certain algorithms, like CNNs and GCNs, have trouble extracting temporal correlations, others, like RNNs and their variations, have trouble extracting spatial dependencies.…”

mentioning

confidence: 99%

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A spatial‐temporal graph gated transformer for traffic forecasting

Bouchemoukha,

Zennir,

Alioua

2024

Trans Emerging Tel Tech

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Accurate traffic forecasting is more necessary than ever for transportation departments, especially given its significant role in traffic planning, management, and control. However, most existing methods struggle to address complex spatial correlations on road networks, nonlinear temporal dynamics, and difficult long‐term prediction. This article proposes a novel spatial temporal graph gated transformer (STGGT) to overcome these challenges. The suggested model differs from Google's transformer because it uses a hybrid architecture that integrates graph convolutional networks (GCNs), attention, and gated recurrent units (GRUs) instead of solely relying on attention. Specifically, STGGT uses GCNs to extract spatial dependencies, utilizes attention and GRUs to extract temporal dependencies, and handle long‐term prediction. Experiments indicate that STGGT outperforms the state‐of‐the‐art baseline models on two real‐world traffic datasets of 9%–40%. The proposed model offers a promising solution for accurate traffic forecasting, simultaneously addressing the challenges of complex spatial correlations, nonlinear temporal dynamics, and long‐term prediction.

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Section: Methodsmentioning

confidence: 99%

Section: Spatial and Temporal Neural Networkmentioning

confidence: 99%

Section: Spatial-temporal Attention Networkmentioning

confidence: 99%

mentioning

confidence: 99%

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A spatial‐temporal graph gated transformer for traffic forecasting

Bouchemoukha,

Zennir,

Alioua

2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…As mentioned above traffic-flow prediction providing typical time-series prediction is one of the essential parts of ITS, which is still a challenging issue. Over the past decades, various models and techniques were employed in traffic-flow prediction, which can be roughly divided into model-driven and data-driven approaches [16,17].…”

Section: Literature Reviewmentioning

confidence: 99%

STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction

2023

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As an important component of intelligent transportation-management systems, accurate traffic-parameter prediction can help traffic-management departments to conduct effective traffic management. Due to the nonlinearity, complexity, and dynamism of highway-traffic data, traffic-flow prediction is still a challenging issue. Currently, most spatial–temporal traffic-flow-prediction models adopt fixed-structure time convolutional and graph convolutional models, which lack the ability to capture the dynamic characteristics of traffic flow. To address this issue, this paper proposes a spatial–temporal prediction model that can capture the dynamic spatial–temporal characteristics of traffic flow, named the spatial–temporal self-attention graph convolutional network (STA-GCN). In terms of feature engineering, we used the time cosine decomposition and one-hot encoding methods to capture the periodicity and heterogeneity of traffic-flow changes. Additionally, in order to build the model, self-attention mechanisms were incorporated into the spatial–temporal convolution to capture the spatial–temporal dynamic characteristics of traffic flow. The experimental results indicate that the performance of the proposed model on two traffic-volume datasets is superior to those of several baseline models. In particular, in long-term prediction, the prediction error can be reduced by over 5%. Further, the interpretability and robustness of the prediction model are addressed by considering the spatial dynamic changes.

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A multi-scale spatiotemporal network traffic prediction method based on spiking neural model

Li,

Peng

et al. 2024

J Membr Comput

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Spatial‐temporal correlation graph convolutional networks for traffic forecasting

Abstract: This is a repository copy of Spatial-temporal correlation graph convolutional networks for traffic forecasting.

Cited by 4 publications

References 52 publications

A spatial‐temporal graph gated transformer for traffic forecasting

A spatial‐temporal graph gated transformer for traffic forecasting

STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction

A multi-scale spatiotemporal network traffic prediction method based on spiking neural model

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