Traffic Flow Prediction Model Based on the Combination of Improved Gated Recurrent Unit and Graph Convolutional Network

Zhao, Yun; Han, Xue; Xu, Xing

doi:10.3389/fbioe.2022.804454

Cited by 5 publications

(6 citation statements)

References 28 publications

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“…Given the limited information available, one cannot draw any firm conclusions about junction 4. In [30], GRU based DNN was implemented with RMSE result of 14.21, 39.99, 27.46, 8.84, and 5.46 in the situations: offpeak, peak, complete dataset, losloop, and Shenzhen road, respectively. In [27], the RMSE for five sections were 31.847, 29.035, 19.352, 68.392, and 81.394, for the first to fifth sections, respectively in the United Kingdom traffic flow dataset, where the LSTM based DNN was implemented.…”

Section: Resultsmentioning

confidence: 99%

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Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

et al. 2023

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Congestion in the world's traffic systems is a major issue that has far-reaching repercussions, including wasted time and money due to longer commutes and more frequent stops for gas. Modern scholarly challenges arise alongside chances to greatly enhance traffic prediction made possible by the integration of modern technologies into transportation systems. Various techniques have been utilized for the purpose of traffic flow prediction, including statistical, machine learning, and deep neural networks. In this paper, deep neural network architecture based on long short term memory (LSTM), bi-directional version, and gated recurrent units (GRUs) layers have been structured to build the deep neural network, in order to predict the performance of the traffic flow in four distinct junctions which has a great impact on the Internet of vehicles' applications. The structure comprised of sixteen-layers, five of them are GRU-layers and one bi-directional LSTM-layer. The dataset employed in this work involved four congested junctions. The dataset extended from the first of November 2016 to 30th of June 2017. Cleaning and preprocessing operations were achieved on the dataset before feeding it to the designed deep neural network of this paper. Results show that the suggested method produced a comparable performance with respect to state-of-the art approaches.

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Section: Resultsmentioning

confidence: 99%

“…The study in [30] put up a plan for the TFP of Hangzhou's Wenyi Road. There are four intersections along Wenyi Road.…”

Section: Related Workmentioning

confidence: 99%

Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

et al. 2023

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“…Subsequently, graph convolutional networks began to be widely used in traffic flow prediction. Zhao et al [6] proposed a method that combines graph convolutional networks with improved gated recurrent units (GRUs). They straightforwardly concatenate the improved GRU and GCN to achieve the extraction of spatio-temporal dependencies in the data.…”

Section: B Spatio-temporal Information For Traffic Flow Predictionmentioning

confidence: 99%

“…However, modeling solely based on temporal features has certain limitations. The methods that combine graph convolutional networks (GCNs) with recurrent neural networks have gradually become a research hotspot [5,6]. Such methods typically represent the static road network structure as a topological graph and utilize GCNs to learn the spatial correlation between nodes.…”

Section: Introductionmentioning

confidence: 99%

Spatial–Temporal Traffic Flow Prediction With Fusion Graph Convolution Network and Enhanced Gated Recurrent Units

Cai,

Qu,

et al. 2024

IEEE Access

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Accurately predicting traffic flow is paramount for the efficient operation of transportation systems. The key to enhancing prediction accuracy lies in effectively mining the intricate spatio-temporal correlations within traffic flow data. However, traditional traffic flow prediction methods that combine Graph Convolutional Network and Recurrent Neural Network have limitations in capturing comprehensive spatial correlation information and face challenges in modeling long-term temporal dependencies, consequently leading to suboptimal prediction performance. This study proposes a hybrid traffic flow prediction model based on fusion graph convolutional network and enhanced gate recurrent unit. Initially, a fusion graph structure is constructed based on adjacency graph and adaptive graph to better represent the correlations between nodes in the road network. Subsequently, the stacked fusion graph convolution module is utilized to capture multi-level spatial correlations and the enhanced gated recurrent unit is applied to extract multi-scale temporal correlations. In addition, the model integrates the extracted spatio-temporal features with the direct features through residual connection units, and utilizes the fused features for prediction, achieving superior predictive performance. The experimental results from four authentic datasets demonstrate that our proposed model outperforms state-of-the-art baseline models, showcasing an average enhancement of 3% in Mean Absolute Error(MAE), 3.3% in Root Mean Square Error(RMSE), and 2.7% in Mean Absolute Percentage Error(MAPE) across the four datasets.

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“…Road traffic flow exhibits time-space characteristics [7][8][9]. To enhance road traffic efficiency, scholars have conducted extensive research on traffic flow characteristics and developed numerous models, including the cellular automata model [10,11] and the viscoelastic model [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Coupling Relationship between Urban Road Spatial Structure and Traffic Flow

et al. 2023

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In order to promote the sustainable development of urban traffic systems, improve the accuracy of traffic system analysis in the urban planning stage and reduce the possibility of traffic congestion in the operation stage of road networks, the coupling relationship and evolution mechanism between urban road spatial structure and traffic flow were studied, and a model of the relationship between the metrics was established in this study based on real road network and traffic flow data. First, the road spatial structure model of the study area was established from the perspective of road space, and the spatial syntax method was applied to verify the rationality of the spatial structure of the road network. Secondly, the initial OD matrix was determined by OD backpropagation based on the measured traffic flow data. Thirdly, the coupling rule between the spatial structure and the traffic flow of the road network was explored by loading the increment in the OD matrix to the initial OD matrix step by step based on a simulation experiment. Finally, the relationship between the degree of integration of the spatial syntactic feature parameter and the saturation of the traffic flow feature parameter was modelled on the basis of experimental results and verified by an example. This research shows that the spatial structure of urban roads has a significant impact on the characterisation of the traffic flow distribution of road networks, and a strong correlation can be found between the integration degree and saturation degree. An optimal fit, which can be used as a reference for the design of road spatial structure, was explored in this research.

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Traffic Flow Prediction Model Based on the Combination of Improved Gated Recurrent Unit and Graph Convolutional Network

Cited by 5 publications

References 28 publications

Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

Spatial–Temporal Traffic Flow Prediction With Fusion Graph Convolution Network and Enhanced Gated Recurrent Units

Modelling the Coupling Relationship between Urban Road Spatial Structure and Traffic Flow

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