Spatial–Temporal Complex Graph Convolution Network for Traffic Flow Prediction

Bao, Yinxin; Huang, Jiashuang; Shen, Qin-Qin; Cao, Yang; Ding, Weiping; Shi, Zhenquan; Shi, Quan

doi:10.1016/j.engappai.2023.106044

Cited by 34 publications

(13 citation statements)

References 49 publications

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“…Zhao et al [41] considered spatio-temporal depth relations mining at the location level. Bao et al [42] characterized dynamic spatio-temporal features by constructing complex correlation matrices through multi-feature and attention mechanisms, based on spatio-temporal complex graph convolutional networks. However, these investigations only assessed distance and disregarded the spatio-temporal structure of the graph, as well as the impact of the surroundings components on traffic flow, resulting in the inability to make accurate traffic predictions.…”

Section: Space-time Traffic Forecastingmentioning

confidence: 99%

Weather Interaction-Aware Spatio-Temporal Attention Networks for Urban Traffic Flow Prediction

Zhong,

Wang,

Chen

et al. 2024

Buildings

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As the cornerstone of intelligent transportation systems, accurate traffic prediction can reduce the pressure of urban traffic, reduce the cost of residents’ travel time, and provide a reference basis for urban construction planning. Existing traffic prediction methods focus on spatio-temporal dependence modeling, ignoring the influence of weather factors on spatio-temporal characteristics, and the prediction task has complexity and an uneven distribution in different spatio-temporal scenarios and weather changes. In view of this, we propose a weather interaction-aware spatio-temporal attention network (WST-ANet), in which we integrate feature models and dynamic graph modules in the encoder and decoder, and use a spatio-temporal weather interaction perception module for prediction. Firstly, the contextual semantics of the traffic flows are fused using a feature embedding module to improve the adaptability to weather drivers; then, an encoder–decoder is constructed by combining the Dynamic Graph Module and the WSTA Block, to extract spatio-temporal aggregated correlations in the roadway network; finally, the feature information of the encoder was weighted and aggregated using the cross-focusing mechanism, and attention was paid to the hidden state of the encoding. Traffic flow was predicted using the PeMS04 and PeMS08 datasets and compared with multiple typical baseline models. It was learned through extensive experiments that the accuracy evaluation result is the smallest in WST-ANet, which demonstrated the superiority of the proposed model. This can more accurately predict future changes in traffic in different weather conditions, providing decision makers with a basis for optimizing scenarios.

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Section: Space-time Traffic Forecastingmentioning

confidence: 99%

Weather Interaction-Aware Spatio-Temporal Attention Networks for Urban Traffic Flow Prediction

Zhong,

Wang,

Chen

et al. 2024

Buildings

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“…In [44], the researcher proposes a PCNN model, which uses vehicle passage records from the surveillance cameras on roads, to predict the short-term traffic congestion. In [45], the authors propose a spatial-temporal complex graph convolution network (ST-CGCN) to predict traffic. The authors first generate a complex correlation matrix for spatial and temporal feature and then feed it to a 3D convolution operator followed by LSTM.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast to [42][43][44][45][46], where the authors had to go through a complex process to incorporate spatial-temporal features of the dataset into the model, in [47], the author presents a simple and straightforward algorithm without any complex data-preprocessing for traffic congestion prediction. Building on the simplistic design presented in [47], in this study, we replace the initial CNN blocks with the HRNet architecture to learn multi-scale feature representation of the input dataset and replace the LSTM layer by Convolutional LSTM layer for learning spatial and temporal information.…”

Section: Related Workmentioning

confidence: 99%

Large-Scale Road Network Traffic Congestion Prediction Based on Recurrent High-Resolution Network

et al. 2023

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Traffic congestion is a significant problem that adversely affects the economy, environment, and public health in urban areas worldwide. One promising solution is to forecast road-level congestion levels in the short-term and long-term, enabling commuters to avoid congested areas and allowing traffic agencies to take appropriate action. In this study, we propose a hybrid deep neural network algorithm based on High-Resolution Network (HRNet) and ConvLSTM decoder for 10, 30, and 60-min traffic congestion prediction. Our model utilizes the HRNet’s multi-scale feature extraction capability to capture rich spatial features from a sequence of past traffic input images. The ConvLSTM module learns temporal information from each HRNet multi-scale output and aggregates all feature maps to generate accurate traffic forecasts. Our experiments demonstrate that the proposed model can efficiently and effectively learn both spatial and temporal relationships for traffic congestion and outperforms four other state-of-the-art architectures (PredNet, UNet, ConvLSTM, and Autoencoder) in terms of accuracy, precision, and recall. A case study was conducted on the dataset from Seoul, South Korea.

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“…[16,17]. Nonetheless, most of these methods are not well-suited for handling large, dynamic, non-stationary data, which depicts its space and how it changes over time [18][19][20][21]. Deep Neural Networks (DNNs), on the other hand, are applicable due to their ability to handle immense amounts of data and their capability of modeling complex relationships, both spatially and temporally [22].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

et al. 2023

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Cyber-physical systems (CPS) refer to systems that integrate communication, control, and computational elements into physical processes to facilitate the control of physical systems and effective monitoring. The systems are designed to interact with the physical world, monitor and control the physical processes while in operation, and generate data. Deep Neural Networks (DNN) comprise multiple layers of interconnected neurons that process input data to produce predictions. Spatial-temporal data represents the physical world and its evolution over time and space. The generated spatial-temporal data is used to make decisions and control the behavior of CPS. This paper systematically reviews the applications of DNNs, namely convolutional, recurrent, and graphs, in handling spatial-temporal data in CPS. An extensive literature survey is conducted to determine the areas in which DNNs have successfully captured spatial-temporal data in CPS and the emerging areas that require attention. The research proposes a three-dimensional framework that considers: CPS (transportation, manufacturing, and others), Target (spatial-temporal data processing, anomaly detection, predictive maintenance, resource allocation, real-time decisions, and multi-modal data fusion), and DNN schemes (CNNs, RNNs, and GNNs). Finally, research areas that need further investigation are identified, such as performance and security. Addressing data quality, strict performance assurance, reliability, safety, and security resilience challenges are the areas that are required for further research.

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Spatial–Temporal Complex Graph Convolution Network for Traffic Flow Prediction

Cited by 34 publications

References 49 publications

Weather Interaction-Aware Spatio-Temporal Attention Networks for Urban Traffic Flow Prediction

Weather Interaction-Aware Spatio-Temporal Attention Networks for Urban Traffic Flow Prediction

Large-Scale Road Network Traffic Congestion Prediction Based on Recurrent High-Resolution Network

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

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