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Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

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Computers, Materials & Continua 2024年第1期78卷 265-282页

作者： Ming Luo Huili Dou Ning Zheng School of Computer Science Hangzhou Dianzi UniversityHangzhou310018China Zhejiang Institute of Communications Hangzhou310012China School of Cyberspace Security Hangzhou Dianzi UniversityHangzhou310018China

Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic *** well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is *** order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic *** S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the *** terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution *** order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph *** data is output using a fully connected layer at the *** findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to *** experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.

关键词： Urban traffic traffic temporal correlation GNN prediction

TGAIN: Geospatial Data Recovery Algorithm Based on GAIN-LSTM

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Computers, Materials and Continua 2024年第1期81卷 1471-1489页

作者： Yang L. Li L. Ma S. School of Soft Engineering Jinling Institute of Technology Nanjing 211169 China School of Computer Jinling Institute of Technology Nanjing 211169 China School of Network Security Jinling Institute of Technology Nanjing 211169 China

Accurate geospatial data are essential for geographic information systems (GIS), environmental monitoring, and urban planning. The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data. In this paper, we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery. Existing geospatial data recovery methods require complete datasets for training, resulting in time-consuming data recovery and lack of generalization. To address these issues, we propose a GAIN-LSTM-based geospatial data recovery method (TGAIN), which consists of two main works: (1) it uses a long-short-term recurrent neural network (LSTM) as a generator to analyze geospatial temporal data and capture its temporal correlation; (2) it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input data. The experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), mean absolute percentage error (MAPE), coefficient of determination (R2) and average computational time across various data missing rates. Concurrently, TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models, especially when dealing with a high rate of missing data. Our model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis. © 2024 The Authors. Published by Tech Science Press.

关键词： data recovery generative adversarial networks Geospatial data temporal correlation

Quantitative assessment of the spatio-temporal correlations of seismic events induced by longwall coal mining

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第5期14卷 1406-1420页

作者： Shuyu Wang Guangyao Si Changbin Wang Wu Cai Binglei Li Joung Oh Ismet Canbulat School of Minerals and Energy Resources Engineering University of New South WalesSydney2052Australia State Key Laboratory of Coal Resources and Safe Mining School of MinesChina University of Mining and TechnologyXuzhou221116China Zijin School of Geology and Mining Fuzhou UniversityFuzhou350108China

Rock failure process as a natural response to mining activities is associated with seismic events, which can pose a potential hazard to mine operators, equipment and infrastructures. Mining-induced seismicity has been found to be internally correlated in both time and space domains as a result of rock fracturing during progressive mining activities. Understanding the spatio-temporal(ST) correlation of mininginduced seismic events is an essential step to use seismic data for further analysis, such as rockburst prediction and caving assessment. However, there are no established methods to perform this critical task. Input parameters used for the prediction of seismic hazards, such as the time window of past data and effective prediction distance, are determined based on site-specific experience without statistical or physical reasons to support. Therefore, the accuracy of current seismic prediction methods is largely constrained, which can only be addressed by quantitively assessing the ST correlations of mininginduced seismicity. In this research, the ST correlation of seismic event energy collected from a study mine is quantitatively analysed using various statistical methods, including autocorrelation function(ACF), semivariogram and Moran’s I analysis. In addition, based on the integrated ST correlation assessment, seismic events are further classified into seven clusters, so as to assess the correlations within individual clusters. The correlation of seismic events is found to be quantitatively assessable, and their correlations may vary throughout the mineral extraction process.

关键词： Spatial correlation temporal correlation Autocorrelation function(ACF) Semivariogram Scale of fluctuation

Physics-guided Deep Learning for Power System State Estimation

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Journal of Modern Power Systems and Clean Energy 2020年第4期8卷 607-615页

作者： Lei Wang Qun Zhou Shuangshuang Jin Department of Electrical and Computer Engineering University of Central FloridaOrlandoFL 32816USA School of Computing Clemson UniversityNorth CharlestonSC 29405USA

In the past decade,dramatic progress has been made in the field of machine *** paper explores the possibility of applying deep learning in power system state ***,physics-based models are used including weighted least square(WLS)or weighted least absolute value(WLAV).These models typically consider a single snapshot of the system without capturing temporal correlations of system *** this paper,a physics-guided deep learning(PGDL)method is ***,inspired by autoencoders,deep neural networks(DNNs)are used to learn the temporal *** estimated system states from DNNs are then checked against physics laws by running through a set of power flow ***,the proposed PGDL is both data-driven and *** accuracy and robustness of the proposed PGDL method are compared with traditional methods in standard IEEE *** show promising results and the applicability is further discussed.

关键词： State estimation deep learning deep neural network(DNN) temporal correlation power system

Information Driven Data Gathering for Energy Efficient Wireless Sensor Network

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Circuits and Systems 2016年第11期7卷 3886-3895页

作者： K. Anna Raja L. R. Karlmarx Department of Electrical and Electronics PTR College of Engineering and Technology Madurai India Department of Electronics and communication Thiagarajar College of Engineering Madurai India

Large scale dense Wireless Sensor Networks (WSNs) have been progressively employed for different classes of applications for the resolve of precise monitoring. As a result of high density of nodes, both spatially and temporally correlated information can be detected by several nodes. Hence, energy can be saved which is a major aspect of these networks. Moreover, by using these advantages of correlations, communication and data exchange can be reduced. In this paper, a novel algorithm that selects the data based on their contextual importance is proposed. The data, which are contextually important, are only transmitted to the upper layer and the remains are ignored. In this way, the proposed method achieves significant data reduction and in turn improves the energy conservation of data gathering.

关键词： Wireless Sensor Network Energy Efficiency Data Reduction temporal correlation

temporal and spatial correlations of spike trains in frog retinal ganglion cells

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Temporal and spatial correlations of spike trains in frog re...