Traffic Speed Prediction Based on LSTM-Graph Attention Network (L-GAT)

Fang, Yujie; Jiang, Jie; He, Yixiang

doi:10.1109/aemcse51986.2021.00163

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…The update gate aids in capturing long-term dependencies in the time series, while the reset gate is in charge of eliminating old data that is unrelated to future states. The GRU layer may be stated as Equations ( 9) and (10) for an input feature i t :…”

Section: Temporal Dependencymentioning

confidence: 99%

“…By considering the interdependencies between different road segments and their influence on each other, GNNs can learn complex patterns and make accurate predictions. In order to accurately predict the traffic speed, several studies on GNNs (Graph Neural Networks) [8,9] and GCNNs (Graph CNNs) [10] have generated novel concepts. This is the inspiration behind this study, where the problem is formulated considering both input and output as a sequential graph.…”

Section: Introductionmentioning

confidence: 99%

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A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities

Sharma

Nikashina

et al. 2023

Sustainability

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Planning effective routes and monitoring vehicle traffic are essential for creating sustainable smart cities. Accurate speed prediction is a key component of these efforts, as it aids in alleviating traffic congestion. While their physical proximity is important, the interconnection of these road segments is what significantly contributes to the increase of traffic congestion. This interconnectedness poses a significant challenge to increasing prediction accuracy. To address this, we propose a novel approach based on Deep Graph Neural Networks (DGNNs), which represent the connectedness of road sections as a graph using Graph Neural Networks (GNNs). In this study, we implement the proposed approach, called STGGAN, for real-time traffic-speed estimation using two different actual traffic datasets: PeMSD4 and PeMSD8. The experimental results validate the prediction accuracy values of 96.67% and 98.75% for the PeMSD4 and PeMSD8 datasets, respectively. The computation of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) also shows a progressive decline in these error values with increasing iteration count, demonstrating the success of the suggested technique. To confirm the feasibility, reliability, and applicability of the suggested STGGAN technique, we also perform a comparison analysis, including several statistical, analytical, and machine-learning- and deep-learning-based approaches. Our work contributes significantly to the field of traffic-speed estimation by considering the structure and characteristics of road networks through the implementation of DGNNs. The proposed technique trains a neural network to accurately predict traffic flow using data from the entire road network. Additionally, we extend DGNNs by incorporating Gated Graph Attention Network (GGAN) blocks, enabling the modification of the input and output to sequential graphs. The prediction accuracy of the proposed model based on DGNNs is thoroughly evaluated through extensive tests on real-world datasets, providing a comprehensive comparison with existing state-of-the-art models for traffic-flow forecasting.

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Section: Temporal Dependencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities

Sharma

Nikashina

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…The GAT model has been applied in many fields. To be specific, Fang et al [27] proposed a new traffic network speed prediction model named L-GAT, which can capture the spatial characteristics and the temporal dynamics of the traffic network. Based on GAT, Cai et al [28] proposed an unsupervised model named DQ-GAT, which can achieve scalable and proactive autonomous driving.…”

Section: Graph Attention Networkmentioning

confidence: 99%

Med-Tree: A Medical Ontology Tree Combined with the Graph Attention Networks for Medication Recommendation

Yue

Zhang

et al. 2022

Electronics

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Medication recommendation based on Electronic Health Records (EHRs) is a significant research direction in the field of intelligent medicine, which aims to recommend personalized medication combinations for patients based on their historical and current physical conditions. However, since the structural and temporal characteristics of medical records are affected by many uncertain factors, there are many limitations in medication recommendation methods based on EHRs. Specifically, most existing works either fail to adequately assess the structural correlation and temporal dependency among various medical entities or ignore existing knowledge of Drug–Drug Interactions (DDI), which could lead to adverse outcomes. These factors contribute to poor recommendation quality. Therefore, we propose a medical ontology tree model combined with the Graph Attention Networks (GAT) for medication recommendations. First, the class hierarchy extracted from the medical ontology and the GAT model is used to learn the ICD-9 codes of diagnoses and procedures, which enriches the semantic representation of medical entities. Secondly, Gate Recurrent Units (GRU) are used to learn the temporal characteristics of medical entities. Finally, memory bank, dynamic memory and DDI graph are used to optimize the hidden layer results, which improve the accuracy of the model. Experimental results show that the proposed model is superior to the previous methods in all evaluation indicators, and the recommended results have a lower DDI rate.

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Spatial linear transformer and temporal convolution network for traffic flow prediction

Xing,

Huang,

et al. 2024

Sci Rep

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Accurately obtaining accurate information about the future traffic flow of all roads in the transportation network is essential for traffic management and control applications. In order to address the challenges of acquiring dynamic global spatial correlations between transportation links and modeling time dependencies in multi-step prediction, we propose a spatial linear transformer and temporal convolution network (SLTTCN). The model is using spatial linear transformers to aggregate the spatial information of the traffic flow, and bidirectional temporal convolution network to capture the temporal dependency of the traffic flow. The spatial linear transformer effectively reduces the complexity of data calculation and storage while capturing spatial dependence, and the time convolutional network with bidirectional and gate fusion mechanisms avoids the problems of gradient vanishing and high computational cost caused by long time intervals during model training. We conducted extensive experiments using two publicly available large-scale traffic data sets and compared SLTTCN with other baselines. Numerical results show that SLTTCN achieves the best predictive performance in various error measurements. We also performed attention visualization analysis on the spatial linear transformer, verifying its effectiveness in capturing dynamic global spatial dependency.

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Traffic Speed Prediction Based on LSTM-Graph Attention Network (L-GAT)

Cited by 7 publications

References 16 publications

A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities

A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities

Med-Tree: A Medical Ontology Tree Combined with the Graph Attention Networks for Medication Recommendation

Spatial linear transformer and temporal convolution network for traffic flow prediction

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