Deep spatio-temporal 3D dilated dense neural network for traffic flow prediction

He, Rui; Zhang, Cuijuan; Xiao, Yunpeng; Lu, Xingyu; Zhang, Song; Liu, Yanbing

doi:10.1016/j.eswa.2023.121394

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…In the field of traffic volume forecasting, models can broadly be categorized into parametric and non-parametric based on their structural foundation. Moreover, within the domain of deep learning methodologies, models are subclassified into generative, discriminative, and hybrid deep structures, each demonstrating its unique capabilities and advancements over time [11]. The evolution of research has seen a shift from traditional parametric statistical models towards non-parametric and subsequently to hybrid models, indicating a progression towards more complex and nuanced modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns

Han,

Neira-Molina,

Khan

et al. 2024

J Cloud Comp

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In this study, we present the EEG-GCN, a novel hybrid model for the prediction of time series data, adept at addressing the inherent challenges posed by the data's complex, non-linear, and periodic nature, as well as the noise that frequently accompanies it. This model synergizes signal decomposition techniques with a graph convolutional neural network (GCN) for enhanced analytical precision. The EEG-GCN approaches time series data as a one-dimensional temporal signal, applying a dual-layered signal decomposition using both Ensemble Empirical Mode Decomposition (EEMD) and GRU. This two-pronged decomposition process effectively eliminates noise interference and distills the complex signal into more tractable sub-signals. These sub-signals facilitate a more straightforward feature analysis and learning process. To capitalize on the decomposed data, a graph convolutional neural network (GCN) is employed to discern the intricate feature interplay within the sub-signals and to map the interdependencies among the data points. The predictive model then synthesizes the weighted outputs of the GCN to yield the final forecast. A key component of our approach is the integration of a Gated Recurrent Unit (GRU) with EEMD within the GCN framework, referred to as EEMD-GRU-GCN. This combination leverages the strengths of GRU in capturing temporal dependencies and the EEMD's capability in handling non-stationary data, thereby enriching the feature set available for the GCN and enhancing the overall predictive accuracy and stability of the model. Empirical evaluations demonstrate that the EEG-GCN model achieves superior performance metrics. Compared to the baseline GCN model, EEG-GCN shows an average R2 improvement of 60% to 90%, outperforming the other methods. These results substantiate the advanced predictive capability of our proposed model, underscoring its potential for robust and accurate time series forecasting.

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Section: Introductionmentioning

confidence: 99%

Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns

Han,

Neira-Molina,

Khan

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

show abstract

“…Moreover, Chen et al [21] developed a Traffic Flow Matrix-Based Graph Neural Network (TFM-GCAM) that employs a novel graph convolution strategy enhanced with attention mechanisms to improve the accuracy of traffic flow prediction. He et al [22] presented a 3D dilated dense neural network that leverages multiscale dilated convolutions to address the spatiotemporal variations in traffic data more dynamically. Lastly, Bao et al [23] introduced the Spatial-Temporal Complex Graph Convolution Network (ST-CGCN), which uses a complex correlation matrix to model the intricate relationships between traffic nodes, thereby enhancing both the spatial and temporal feature extraction capabilities.…”

Section: Deep Learning In Traffic Predictionmentioning

confidence: 99%

Traffic Prediction with Self-Supervised Learning: A Heterogeneity-Aware Model for Urban Traffic Flow Prediction Based on Self-Supervised Learning

Gao,

Wei,

Xie

et al. 2024

Mathematics

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Accurate traffic prediction is pivotal when constructing intelligent cities to enhance urban mobility and to efficiently manage traffic flows. Traditional deep learning-based traffic prediction models primarily focus on capturing spatial and temporal dependencies, thus overlooking the existence of spatial and temporal heterogeneities. Heterogeneity is a crucial inherent characteristic of traffic data for the practical applications of traffic prediction. Spatial heterogeneities refer to the differences in traffic patterns across different regions, e.g., variations in traffic flow between office and commercial areas. Temporal heterogeneities refer to the changes in traffic patterns across different time steps, e.g., from morning to evening. Although existing models attempt to capture heterogeneities through predefined handcrafted features, multiple sets of parameters, and the fusion of spatial–temporal graphs, there are still some limitations. We propose a self-supervised learning-based traffic prediction framework called Traffic Prediction with Self-Supervised Learning (TPSSL) to address this issue. This framework leverages a spatial–temporal encoder for the prediction task and introduces adaptive data masking to enhance the robustness of the model against noise disturbances. Moreover, we introduce two auxiliary self-supervised learning paradigms to capture spatial heterogeneities and temporal heterogeneities, which also enrich the embeddings of the primary prediction task. We conduct experiments on four widely used traffic flow datasets, and the results demonstrate that TPSSL achieves state-of-the-art performance in traffic prediction tasks.

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A novel radial basis function neural network classifier based on three-way decisions

Li,

Qiao

2025

Engineering Applications of Artificial Intelligence

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Deep spatio-temporal 3D dilated dense neural network for traffic flow prediction

Cited by 7 publications

References 29 publications

Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns

Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns

Traffic Prediction with Self-Supervised Learning: A Heterogeneity-Aware Model for Urban Traffic Flow Prediction Based on Self-Supervised Learning

A novel radial basis function neural network classifier based on three-way decisions

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