A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB

Zhu, Hailong; Xie, Yawen; He, Wei; Sun, Chao; Kaili, Zhu; Zhou, Guohui; Ma, Ning

doi:10.1155/2020/7586154

Cited by 30 publications

(13 citation statements)

References 23 publications

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“…It can only handle Euclidean data since its translation invariance. RNN [30], LSTM, and GRU are suitable for processing timeseries data [31][32][33]. These networks rely on the sequential temporal order of the data itself.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

A Hybrid Model Integrating Local and Global Spatial Correlation for Traffic Prediction

et al. 2022

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Accurate traffic prediction can effectively alleviate traffic congestion problems. The complex spatial correlation of traffic flow contributes to the challenging prediction problem. Most current prediction methods focus on learning local spatial correlation, ignoring the spatial correlation of long-distance traffic flow. In this paper, we combine the improved Graph Convolutional Network (GCN) with Gated Recurrent Unit (GRU) to propose a hybrid model integrating local and global spatial correlation (T-LGGCN) for traffic prediction. The model consists of two parts: global spatial-temporal component and local spatial-temporal component. For the global spatial-temporal component, we construct the global correlation matrix to improve the GCN for obtaining the global spatial correlation. And GRU is stacked to obtain the global spatial-temporal correlation. For the local spatial-temporal component, we utilize the strategy of combining Fully Connected Layer (FCL) and GCN to analyze the local spatial correlation. Similarly, GRU is used to perform the output of local spatial-temporal correlation. The output of the two components is finally summed, and the prediction results are generated with the dense network. Experiments were conducted using the highway datasets PEMS04 and PEMS08 from the Caltrans Performance Measurement System, and the results show that our model significantly outperforms state-of-the-art baselines.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

A Hybrid Model Integrating Local and Global Spatial Correlation for Traffic Prediction

et al. 2022

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“…Tis model uses external factors as dynamic attributes and static attributes and designs an attribute-augmented unit to encode and integrate those factors into the spatiotemporal graph convolution model and perform trafc speed prediction. In another approach, Zhu et al [104] proposed BRBbased RNN-GCN model for trafc fow prediction, which solves the existing issues of trafc fow prediction models such as saturation or speed. In the scenarios related to trafc prediction, how to solve the spatiotemporal dependence is an important research direction.…”

Section: Gcn For Computer Visionmentioning

confidence: 99%

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Bhatti

Tang

et al. 2023

International Journal of Intelligent Systems

127

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Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real-life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge-based graph, and other areas due to their excellent characteristics in processing non-European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph-based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.

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“…A dataset provided by the University of Minnesota Duluth Data center was utilized to demonstrate the effectiveness of the proposed methods, and the results have indicated that the proposed method outperforms the auto-regressive integrated moving average and wavelet neural network in terms of prediction accuracy. Zhu et al [ 39 ] integrated the GNN with RNN to extract the spatial and temporal correlations of traffic data. The belief rule-based algorithm was adopted for data fusion, and the fused traffic data were fed into the proposed methodology for traffic flow prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Convolutional Long-Short Term Memory Network with Multi-Head Attention Mechanism for Traffic Flow Prediction

Wei

Liu

2022

Sensors

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Accurate predictive modeling of traffic flow is critically important as it allows transportation users to make wise decisions to circumvent traffic congestion regions. The advanced development of sensing technology makes big data more affordable and accessible, meaning that data-driven methods have been increasingly adopted for traffic flow prediction. Although numerous data-driven methods have been introduced for traffic flow predictions, existing data-driven methods cannot consider the correlation of the extracted high-dimensional features and cannot use the most relevant part of the traffic flow data to make predictions. To address these issues, this work proposes a decoder convolutional LSTM network, where the convolutional operation is used to consider the correlation of the high-dimensional features, and the LSTM network is used to consider the temporal correlation of traffic flow data. Moreover, the multi-head attention mechanism is introduced to use the most relevant portion of the traffic data to make predictions so that the prediction performance can be improved. A traffic flow dataset collected from the Caltrans Performance Measurement System (PeMS) database is used to demonstrate the effectiveness of the proposed method.

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A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB

Cited by 30 publications

References 23 publications

A Hybrid Model Integrating Local and Global Spatial Correlation for Traffic Prediction

A Hybrid Model Integrating Local and Global Spatial Correlation for Traffic Prediction

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Convolutional Long-Short Term Memory Network with Multi-Head Attention Mechanism for Traffic Flow Prediction

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