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Traffic congestion predictor: A spatiotemporal graph neural network with congestion-conditional adaptive mechanism and optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 277卷

作者： Liu, Yutian Gong, Jian Huang, Tianjin Cao, Jinde Peking Univ Sch Urban Planning & Design Shenzhen Grad Sch Shenzhen 518055 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Univ Exeter Dept Comp Sci Exeter EX44QJ England Southeast Univ Sch Math Nanjing 211189 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

This paper addresses the underexplored challenge of traffic congestion prediction, a problem frequently encountered in practice yet difficult to model accurately in the literature. Precise congestion prediction is essential for effective traffic management and the implementation of proactive control strategies. To tackle this, we introduce TC-Predictor, a novel neural network architecture that integrates a congestion-conditional adaptive graph convolutional network (GCN). This mechanism captures both the global road network structure and local dynamic patterns, while dynamically adjusting the adjacency matrix based on congestion levels at each prediction step. Additionally, we introduce a data-driven metric that prioritizes infrequent but critical traffic data, thereby emphasizing high-traffic scenarios. Furthermore, the sparrow search algorithm (SSA) is employed for hyperparameter optimization, with an added congestion constraint in its objective function. Empirical evaluations on real-world datasets show that while many deep learning models perform adequately under low congestion, their accuracy significantly deteriorates under high congestion levels. In contrast, TCPredictor consistently outperforms these models, particularly in predicting congestion under heavy traffic conditions.

关键词： Traffic congestion Adaptive mechanism Graph convolution network Traffic prediction Causal inference sparrow search algorithm

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A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model

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SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Jia, Xiaoliang Zhang, Guoyan Wang, Zhiqiang Li, Huacong Hu, Jing Zhu, Songlin Liu, Caiwei Shandong Lu Qiao Grp Co Ltd Jinan 250014 Shandong Peoples R China Qingdao Univ Technol Sch Civil Engn Qingdao 266525 Peoples R China Shandong Zhengyuan Digital City Construct Co Ltd Yantai 264670 Shandong Peoples R China Qingdao Univ Technol Innovat Inst Sustainable Maritime Architecture Res Qingdao 266033 Peoples R China

In the field of Structural Health Monitoring (SHM), complete datasets are fundamental for modal identification analysis and risk prediction. However, data loss due to sensor failures, transmission interruptions, or hardware issues is a common problem. To address this challenge, this study develops a method combining Variational Mode Decomposition (VMD) and sparrow search algorithm (SSA)-optimized Gate Recurrent Unit (GRU) for recovering structural response data. The methodology initially employs Variational Mode Decomposition (VMD) to preprocess and decompose the existing data from the target sensor into Intrinsic Mode Functions (IMFs) and residuals. Subsequently, the Gated Recurrent Unit (GRU) network utilizes data from other sensors to reconstruct the IMFs and residuals, ultimately producing the data reconstruction results. During this process, Singular Spectrum Analysis (SSA) is used to optimize the hyperparameters of the GRU network. To validate the effectiveness of this method, we utilized one month of monitoring data collected from a certain project and a publicly available dataset. On the public dataset, we tested performance at different data loss rates. Results show that, compared to a standalone GRU model and a VMD + GRU model, the VMD + SSA + GRU model's reconstruction data root mean squared error is reduced by 46.61% and 32.57% on average, respectively, while the coefficient of determination increases by 38.74% and 18.50%. The data reconstruction method proposed in this study can accurately capture trends in missing data, without the need for manual hyperparameter tuning, and the reconstruction results are highly consistent with the real data.

关键词： Data Reconstruction Structural Health Monitoring Gate Recurrent Unit Variational Mode Decomposition sparrow search algorithm

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Fault Diagnosis of Wind Turbine Bearing Based on Parameter-Optimized VMD and Multi-feature Fusion

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年第8期50卷 5425-5438页

作者： Lai, Hongfeng Chen, Jiawei Xiahou, Kaishun Li, Mengshi Wu, Qinghua South China Univ Technol Sch Elect Power Engn Wushan St Guangzhou 510641 Guangdong Peoples R China

Deep learning techniques have been widely applied to wind turbine bearing fault diagnosis in recent years. However, current methods often face challenges in feature extraction, which may lead to diagnostic failures when working with limited samples. To address this challenge, this paper proposes a novel fault diagnosis method based on parameter-optimized variational mode decomposition (VMD) and multi-feature fusion. Firstly, an improved sparrow search algorithm is proposed to optimize the decomposition parameters of VMD, achieving more accurate feature extraction. Next, ten prior fault features derived from our experience in fault diagnosis are introduced, which effectively assist deep learning techniques in learning fault patterns. Subsequently, a lightweight one-dimensional convolution neural network (1D-CNN) is employed to extract adaptive features from both the raw signal and VMD modal components. Finally, these adaptive features and the prior features are input into a fully connected layer for fault diagnosis. Through the fusion of numerous features, the proposed method attains exceptional performance and robustness in two experiments, particularly in scenarios with limited samples.

关键词： Wind turbine bearing Fault diagnosis Variational mode decomposition sparrow search algorithm Feature fusion

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A long sequence time-series forecasting model for ship motion attitude based on informer

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OCEAN ENGINEERING 2024年 305卷

作者： Hou, Lingyi Wang, Xiao Sun, Hang Sun, Yuwen Wei, Zhiyuan Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China Dalian Naval Acad Dept Nav Dalian 116018 Peoples R China

Influenced by marine environmental factors, a ship sailing at sea will experience six degrees of freedom oscillating motions. These oscillating motions pose safety risks to ship navigation and maritime operations. Especially in severe weather conditions, these oscillating motions have a significant impact on the takeoff and landing of carrier aircraft, as well as the launch of carrier missiles. Therefore, studying and forecasting the ship motion attitude in wind and waves holds immense significance. This paper presents a sparrow search algorithm (SSA) optimized Informer model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast the long sequence time -series of ship motion attitude. Firstly, the ship motion attitude time series, as measured by the inertial navigation system, is decomposed using CEEMDAN to obtain several intrinsic mode functions (IMFs) and a residual. Then, the IMFs and residual are fed into SSA-Informer, where SSA is used to optimize the hyperparameter of Informer. Finally, the forecast values of IMFs and residual are superimposed and reconstructed to get the ship motion attitude forecasting result. The forecasting experiment under slight, moderate and rough sea conditions in the East China Sea shows that the proposed model achieves better performance and longer forecasting time compared with other popular methods.

关键词： Ship motion forecasting LSTF Informer CEEMDAN sparrow search algorithm

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Detection of Road Rage in Vehicle Drivers Based on Speech Feature Fusion

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IEEE ACCESS 2024年 12卷 174158-174170页

作者： Feng, Xiaofeng Liu, Chenhui Chen, Ying Hunan Police Acad Dept Traff Management Changsha 410138 Peoples R China Hunan Intelligent Secur Inspect Equipment Engn Tec Changsha 410138 Peoples R China Hunan Univ Coll Civil Engn Changsha 410082 Peoples R China Hunan Police Acad Dept Criminal Sci Technol Changsha 410138 Peoples R China

Road rage can easily cause serious road traffic accidents. In order to detect emotional changes of drivers in a straightforward and accurate way, and enable measures to be taken to avoid accidents, this paper presents a road rage emotion detection method called DMG-FCC for drivers of motor vehicles based on the fusion of MFCC and GFCC speech features in the frequency domain and their first-order differential features ( Delta MFCC, Delta GFCC). These features are first analysed, and each feature selected for fusion is determined preliminarily based on experience. To improve the detection efficiency, principal component analysis (PCA) is used to reduce the dimensionality of the fused features. Six principal components are selected as detection features based on the proportion of the variance values of the principal components of each dimension. The sparrow search algorithm is used to optimise the support vector machine classifier, and an SSA-SVM road rage emotion detection model is established and trained to recognise road rage emotions in drivers. The driver emotion dataset is established, and enhanced by changing the speech rate, pitch, and volume, through expansion, on the basis of the original 200 pieces of data, a total of 1400 pieces of data were got for each type of dataset. Experiments show that feature fusion gives better anti-noise performance than other single feature detection methods. Compared with other detection models, our method yields good detection performance, with a high detection accuracy of 95.2% on original dataset, and 96.8% on enhanced dataset, which can meet the real driving requirements.

关键词： Feature extraction Roads Vehicles Emotion recognition Mel frequency cepstral coefficient Speech recognition Accidents Support vector machines Automobiles Frequency-domain analysis search methods Speech audio vehicle driver feature fusion road rage emotion detection sparrow search algorithm

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Elastic analytical method with machine learning for predicting the stratum displacement field induced by shallow tunneling

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2024年 159卷 201-212页

作者： Kong, Fanchao Zhou, Xin Guo, Caixia Lu, Dechun Du, Xiuli North China Elect Power Univ Sch Water Resources & Hydropower Engn Beijing 102206 Peoples R China Beijing Univ Technol Inst Geotech & Underground Engn Beijing Peoples R China

Support vector regression (SVR) with sparrow search algorithm (SSA) is developed as the machine learning (ML) model to predict maximum surface settlement smax caused by tunneling. A novel method for calibrating boundary conditions of analytical solution is proposed, where the maximum surface settlement derived by the analytical method is equal to smax predicted by SSA-SVR method. The elastic analytical solution for stratum displacement of a shallow tunnel is presented by the complex variable method, when the calibrated nonuniform displacement function is applied as the tunnel displacement boundary condition. The proposed analytical solution-machine learning (AM) method can predict the stratum displacement field prior to the tunnel excavation. Seventythree tunnel engineering cases are employed to verify the rationality of the proposed SSA-SVR method in predicting smax. The value of R2 in the training and test process is 0.894 and 0.877, respectively. Taking Heathrow Express Trial Tunnel as an example, the potential of the proposed AM method in predicting stratum displacement is presented where the influence of cohesion strength, internal friction angle, Young's elastic modulus of stratum, tunnel radius and depth are considered. The proposed AM method can well predict the stratum surface settlement trough curve, vertical and horizontal displacement at different positions.

关键词： Analytical solution of stratum displacement Support vector regression sparrow search algorithm Nonuniform displacement function Complex variable method

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An in silico scheme for optimizing the enzymatic acquisition of natural biologically active peptides based on machine learning and virtual digestion

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ANALYTICA CHIMICA ACTA 2024年 1298卷 342419页

作者： Lin, Like Li, Cong Zhang, Tianlong Xia, Chaoshuang Bai, Qiuhong Jin, Lihua Shen, Yehua Northwest Univ Coll Chem & Mat Sci Natl Demonstrat Ctr Expt Chem Educ Key Lab Synthet & Nat Funct MolMinist Educ Xian 710127 Shaanxi Peoples R China Boston Univ Ctr Biomed Mass Spectrometry Chobanian & Avedisian Sch Med Boston MA 02118 USA

Background: As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs. Results: A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm -long shortterm memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models. Significance: The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desi

关键词： Biologically active peptides Machine learning Virtual digested sparrow search algorithm Long short-term memory

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An improved extreme learning machine model for predicting the mechanical property of AZ80 magnesium alloy

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APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING 2024年第8期130卷 591-591页

作者： Gu, Jiahan Jiang, Song Guo, Wenbo Wang, Leilei Zhang, Jianping Univ Shanghai Sci & Technol Sch Mech Engn Shanghai Peoples R China Harbin Inst Technol State Key Lab Urban Water Resource & Environm Harbin Peoples R China

In order to improve the mechanical property prediction accuracy of AZ80 magnesium alloy, sparrow search algorithm (SSA) is optimized by the tent chaotic mapping (TCM) algorithm, yielding TCMSSA, and TCMSSA is employed to improve extreme learning machine (ELM) model for obtaining TCMSSA-ELM model. TCMSSA-ELM model, along with the traditional ELM model, are both utilized to predict the stress of AZ80 magnesium alloy in a temperature range of 523 K to 673 K and a strain rate range of 0.001s- 1 to 1s- 1. Comparative analysis is conducted to evaluate the performance differences between the two models. The results indicate that compared with ELM model, the predicted values of TCMSSA-ELM model are closer to the experimental data and the ideal 45 degrees line, proving the higher prediction accuracy of TCMSSA-ELM model. In addition, through algorithmic improvements, the maximum reduction of MAPE is 85.422% and the maximum determination coefficient reaches 0.99956, implying that TCMSSA greatly improves the prediction precision of ELM model. The related results offer a new optimization strategy and model to predict the mechanical property of AZ80 magnesium alloy in each molding stage, and provide a technical reference for applying intelligent algorithms to alloy design.

关键词： AZ80 magnesium alloy sparrow search algorithm Tent chaotic mapping Extreme learning machine Mechanical property Prediction model

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An evolutionary deep learning model based on EWKM, random forest algorithm, SSA and BiLSTM for building energy consumption prediction

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ENERGY 2024年 288卷

作者： Lei, Lei Shao, Suola Liang, Lixia Zhejiang Sci Tech Univ Sch Civil Engn & Architecture Hangzhou 310018 Peoples R China Zhejiang Acad Bldg Res & Design Co Ltd Hangzhou 310023 Peoples R China Zhejiang Engn Res Ctr Green & Low Carbon Technol B Hangzhou 310018 Peoples R China

Accurate prediction of building energy consumption is crucial to the rational scheme of building energy. Combining the entropy-weighted K-means (EWKM) with the random forest (RF) method, a feature selection (EWKM-RF) method is proposed in this paper. Based on the proposed EWKM-RF method, the classification and feature selection of the energy consumption influencing factors can be achieved exclusively. Meanwhile, based on the EWKM-RF method and the bi-directional long-short-term memory neural network (BiLSTM) optimized by the sparrow search algorithm (SSA), an RF-SSA-BiLSTM prediction model for building energy consumption is established in this paper. As the weight, learning rate, and hidden layer node parameters of the BiLSTM neural network are optimized with the SSA, the constraints for manually adjusting parameters are avoided in the proposed prediction model. To examine the accuracy of the proposed model, energy consumption data of a civil public building in Dalian city are collected and tested. Results show the prediction error of RF-SSA-BiLSTM after feature selection is reduced by 24.55 % in high and low energy consumption months. Compared with RF-BiLSTM, RF-PSO-BiLSTM, and RF-CNN-BiLSTM, the RF-SSA-BiLSTM has strong robustness. The average MAE, RMSE and MAPE values of energy consumption prediction in the four seasons are 1.30, 1.63 and 0.02.

关键词： Building energy consumption prediction Deep learning Bidirectional long short-term memory Feature selection sparrow search algorithm

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Novel Flexible Temperature Sensor Based on Polyvinyl Alcohol Acrylamide Hydrogels with Moisture Content Compensation

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SENSORS AND MATERIALS 2024年第4期36卷 1287-1304页

作者： Bai, Yuan Zhu, Liangkuan Xu, Haoyan Northeast Forestry Univ Sch Comp & Control Engn Harbin 150040 Peoples R China Northeast Forestry Univ Sch Mech & Elect Engn Harbin 150040 Peoples R China

Flexible temperature sensors are of considerable importance in many industrial and domestic fields such as medical care, agriculture, and food. We report a novel flexible temperature sensor based on polyvinyl alcohol acrylamide hydrogels with moisture content compensation. Firstly, the flexible temperature sensor was prepared by using graphene as a thermal conductive filler and polyvinyl alcohol and acrylamide as a matrix. Secondly, because the measurement accuracy of the flexible temperature sensor is considerably affected by the moisture content, a design scheme for the flexible temperature sensor with a moisture content compensation model was proposed. Finally, on the basis of the measured data of the flexible temperature sensor under multiple moisture contents, an improved sparrow search algorithm was used to optimize the backpropagation neural network model to compensate for the error caused by changes in moisture content, so as to improve the measurement accuracy of the sensor. The compensation results show that the proposed scheme can significantly reduce the measurement error of the flexible temperature sensor, enabling accurate, reliable, and sustainable monitoring.

关键词： flexible temperature sensor measurement accuracy moisture content compensation sparrow search algorithm

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