TFGAN: Traffic forecasting using generative adversarial network with multi-graph convolutional network

Khaled, Alkilane; Elsir, Alfateh M. Tag; Shen, Yanming

doi:10.1016/j.knosys.2022.108990

Cited by 36 publications

(13 citation statements)

References 17 publications

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“…This paper proposes an asymmetric semantic segmentation model APFCN in view of the low accuracy of traditional semantic segmentation models for images with inconsistent backgrounds. APFCN uses the five layer hole convolution network [11,12] to extract image contour features on the upper path, the convolution core and hole rate of the five layer hole convolution network [13] are designed according to NHDC strategy, and the multi-scale details of the image are extracted through the convolution and pooling network on the lower path. Finally, the image semantic segmentation accuracy is improved by fusing the two types of features.…”

Section: Construction Of Asymmetric Semantic Segmentation Modelmentioning

confidence: 99%

A multilevel segmentation method of asymmetric semantics based on deep learning

Liu,

Yang

2023

IET Cyber-Phy Sys Theory & Ap

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An asymmetric semantic multi‐level segmentation method based on depth learning is proposed in order to improve the precision and effect of semantic segmentation. A ‘content tree’ structure and an adjacency matrix are constructed to represent the parent‐child relationship between each image sub region in a complete image. Through multiple combinations of spatial attention mechanism and channel attention mechanism, the similarity semantic features of the target object can be selectively aggregated, so as to enhance its feature expression and avoid the impact of significant objects. The asymmetric semantic segmentation model asymmetric pyramid feature convolutional network (APFCN) is constructed, and the path feature extraction and parameter adjustment are realised through APFCN. On the basis of APFCN network, a full convolution network is introduced for end‐to‐end image semantic segmentation. Combining the advantages of convolution network in extracting image features and the advantages of short‐term and short‐term memory network in solving long‐term dependence, an end‐to‐end hybrid depth network is constructed for image semantic multi‐level segmentation. The experimental results show that the mean intersection over Union value and mean pixel accuracy value are higher than that of the literature method, both of which are increased by more than 3%, and the segmentation effect is good.

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Section: Construction Of Asymmetric Semantic Segmentation Modelmentioning

confidence: 99%

A multilevel segmentation method of asymmetric semantics based on deep learning

Liu,

Yang

2023

IET Cyber-Phy Sys Theory & Ap

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“…It is an unsupervised learning framework that is widely applicable, but its overall performance needs to be improved. The STGNN can consider both spatial and temporal dependence and can handle highly nonlinear and complex problems [42]. Meanwhile, the complexity of the model is high, and its application in time series needs improvements.…”

Section: B Representation-learning-based Reasoningmentioning

confidence: 99%

Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning

Fang,

Yu,

Ding

et al. 2023

Mathematics

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Complementarity and substitutability between products are essential concepts in retail and marketing. To achieve this, existing approaches take advantage of knowledge graphs to learn more evidence for inference. However, they often omit the knowledge that lies in the unstructured data. In this research, we concentrate on inferring complementary and substitutable products in e-commerce from mass structured and unstructured data. An improved knowledge-graph-based reasoning model has been proposed which cannot only derive related products but also provide interpretable paths to explain the relationship. The methodology employed in our study unfolds through several stages. First, a knowledge graph refining entities and relationships from data was constructed. Second, we developed a two-stage knowledge representation learning method to better represent the structured and unstructured knowledge based on TransE and SBERT. Then, the relationship inferring problem was converted into a path reasoning problem under the Markov decision process environment by learning a dynamic policy network. We also applied a soft pruning strategy and a modified reward function to improve the effectiveness of the policy network training. We demonstrate the effectiveness of the proposed method on standard Amazon datasets, and it gives about 5–15% relative improvement over the state-of-the-art models in terms of NDCG@10, Recall@10, Precision @10, and HR@10.

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“…An STGCN consists of a graph convolution in the spatial dimension and a one-dimensional standard convolution in the temporal dimension, which capture the hidden spatial features of neighborhood locations and the complex temporal dependencies at different times, respectively. Inspired by the above studies, by combining the spatial heterogeneity, dynamic correlations, and uncertainty of road traffic networks, as well as the non-Euclidean data [31] characteristics of traffic flows, a traffic flow prediction model called IDG-PSAtt is proposed. The combination of an interactive dynamic convolution structure with spatialtemporal convolution and a ProbSSAtt block fully captures the dynamic spatial-temporal features of the input traffic flow time series.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the above studies, by combining the spatial heterogeneity, dynamic correlations, and uncertainty of road traffic networks, as well as the non-Euclidean data [31] characteristics of traffic flows, a traffic flow prediction model called IDG-PSAtt is proposed. The combination of an interactive dynamic convolution structure with spatial-temporal convolution and a ProbSSAtt block fully captures the dynamic spatial-temporal features of the input traffic flow time series.…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Prediction Research Based on an Interactive Dynamic Spatial–Temporal Graph Convolutional Probabilistic Sparse Attention Mechanism (IDG-PSAtt)

Ding,

He,

Huang

et al. 2024

Atmosphere

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Accurate traffic flow prediction is highly important for relieving road congestion. Due to the intricate spatial–temporal dependence of traffic flows, especially the hidden dynamic correlations among road nodes, and the dynamic spatial–temporal characteristics of traffic flows, a traffic flow prediction model based on an interactive dynamic spatial–temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed. Specifically, the IDG-PSAtt model consists of an interactive dynamic graph convolutional network (IL-DGCN) with a spatial–temporal convolution (ST-Conv) block and a probabilistic sparse self-attention (ProbSSAtt) mechanism. The IL-DGCN divides the time series of a traffic flow into intervals and synchronously and interactively shares the captured dynamic spatiotemporal features. The ST-Conv block is utilized to capture the complex dynamic spatial–temporal characteristics of the traffic flow, and the ProbSSAtt block is utilized for medium-to-long-term forecasting. In addition, a dynamic GCN is generated by fusing adaptive and learnable adjacency matrices to learn the hidden dynamic associations among road network nodes. Experimental results demonstrate that the IDG-PSAtt model outperforms the baseline methods in terms of prediction accuracy. Specifically, on METR-LA, the mean absolute error (MAE) and root mean square error (RMSE) induced by IDG-PSAtt for a 60 min forecasting scenario are reduced by 0.75 and 1.31, respectively, compared to those of the state-of-the-art models. This traffic flow prediction improvement will lead to more precise estimates of the emissions produced by mobile sources, resulting in more accurate air quality forecasts. Consequently, this research will greatly support local environmental management efforts.

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TFGAN: Traffic forecasting using generative adversarial network with multi-graph convolutional network

Cited by 36 publications

References 17 publications

A multilevel segmentation method of asymmetric semantics based on deep learning

A multilevel segmentation method of asymmetric semantics based on deep learning

Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning

Traffic Flow Prediction Research Based on an Interactive Dynamic Spatial–Temporal Graph Convolutional Probabilistic Sparse Attention Mechanism (IDG-PSAtt)

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