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STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for traffic prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 226-238页

作者： Zebing Wei Hongxia Zhao Zhishuai Li Xiaojie Bu Yuanyuan Chen Xiqiao Zhang Yisheng Lv Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the Shandong Key Laboratory of Smart Transportation(Preparation) Shandong Jiao Tong UniversityShanghai 200240China the College of Science China University of PetroleumQingdao 266580China the Department of Transportation Engineering School of Transportation Science and TechnologyHarbin Institute of TechnologyHarbin 150090China IEEE

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.

关键词： Deep learning graph neural network(GNN) multistream spatial-temporal feature extraction temporal graph traffic prediction

Optimal Deep Learning Enabled Statistical Analysis Model for traffic prediction

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Computers, Materials & Continua 2022年第9期72卷 5563-5576页

作者： Ashit Kumar Dutta S.Srinivasan S.N.Kumar T.S.Balaji Won Il Lee Gyanendra Prasad Joshi Sung Won Kim Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India Department of Electrical&Electronics Engineering Amal Jyothi College of EngineeringKanjirappallyKerala686518India Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India Department of Electronics and Communication Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Business Administration Hanbat National National UniversityDaejeon34158Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control,navigation,route mapping,etc.The traffic prediction model aims to predict the traffic conditions based on the past traffic data.For more accurate traffic prediction,this study proposes an optimal deep learning-enabled statistical analysis model.This study offers the design of optimal convolutional neural network with attention long short term memory(OCNN-ALSTM)model for traffic prediction.The proposed OCNN-ALSTM technique primarily preprocesses the traffic data by the use of min-max normalization technique.Besides,OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time cases.For enhancing the predictive outcomes of the OCNN-ALSTM technique,the bird swarm algorithm(BSA)is employed to it and thereby overall efficacy of the network gets improved.The design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the work.The experimental validation of the OCNNALSTM technique is performed using benchmark datasets and the results are examined under several aspects.The simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions.

关键词： Statistical analysis predictive models deep learning traffic prediction bird swarm algorithm

Parameter Tuned Deep Learning Based traffic Critical prediction Model on Remote Sensing Imaging

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Computers, Materials & Continua 2023年第5期75卷 3993-4008页

作者： Sarkar Hasan Ahmed Adel Al-Zebari Rizgar R.Zebari Subhi R.M.Zeebaree Computer Networks Department Sulaimani Polytechnic UniversitySulaimaniIraq Information Technology Department Technical College of Informatics-AkreDuhok Polytechnic UniversityIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Remote sensing(RS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related features.RS has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features.This article introduces an Optimal Deep Learning for traffic Critical prediction Model on High-Resolution Remote Sensing Images(ODLTCP-HRRSI)to resolve these issues.The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities.To attain this,the presented ODLTCP-HRRSI model performs two major processes.At the initial stage,the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic flow.Next,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)algorithm.The experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.

关键词： Remote sensing images traffic prediction deep learning smart cities intelligent transportation systems

A Nonlinear Spatiotemporal Optimization Method of Hypergraph Convolution Networks for traffic prediction

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Intelligent Automation & Soft Computing 2023年第9期37卷 3083-3100页

作者： Difeng Zhu Zhimou Zhu Xuan Gong Demao Ye Chao Li Jingjing Chen Hangzhou Innovation Institute Beihang UniversityHangzhou310051China Yuanpei College Shaoxing UniversityShaoxing312000China Department of Computer Science Zhijiang College of Zhejiang University of TechnologyShaoxing312000China School of Economics Fudan UniversityShanghai200433China

traffic prediction is a necessary function in intelligent transporta-tion systems to alleviate traffic congestion.Graph learning methods mainly focus on the spatiotemporal dimension,but ignore the nonlinear movement of traffic prediction and the high-order relationships among various kinds of road segments.There exist two issues:1)deep integration of the spatiotempo-ral information and 2)global spatial dependencies for structural properties.To address these issues,we propose a nonlinear spatiotemporal optimization method,which introduces hypergraph convolution networks(HGCN).The method utilizes the higher-order spatial features of the road network captured by HGCN,and dynamically integrates them with the historical data to weigh the influence of spatiotemporal dependencies.On this basis,an extended Kalman filter is used to improve the accuracy of traffic prediction.In this study,a set of experiments were conducted on the real-world dataset in Chengdu,China.The result showed that the proposed method is feasible and accurate by two different time steps.Especially at the 15-minute time step,compared with the second-best method,the proposed method achieved 3.0%,11.7%,and 9.0%improvements in RMSE,MAE,and MAPE,respectively.

关键词： Intelligent transportation systems traffic prediction hypergraph convolution networks spatiotemporal optimization

Prophet model and Gaussian process regression based user traffic prediction in wireless networks

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Science China(Information Sciences) 2020年第4期63卷 207-214页

作者： Yu LI Ziang MA Zhiwen PAN Nan LIU Xiaohu YOU National Mobile Communications Research Laboratory Southeast University

User traffic prediction is an important topic for wireless network operators. A user traffic prediction method based on Prophet and Gaussian process regression is proposed in this paper. The proposed method first employs discrete wavelet transform to decompose the user traffic time series to high-frequency component and low-frequency component. The low-frequency component bears the long-range dependence of user network traffic, while the high-frequency component reveals the gusty and irregular fluctuations of user network traffic. Then Prophet model and Gaussian process regression are applied to predict the two components respectively based on the characteristics of the two components. Experimental results demonstrate that the proposed model outperforms the existing time series prediction method.

关键词： wireless networks traffic prediction prophet model Gaussian process regression

Extreme gradient boosting algorithm based urban daily traffic index prediction model:a case study of Beijing,China

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Digital Transportation and Safety

Digital Transportation and Safety 2023年第3期2卷 220-228页

作者： Jiancheng Weng Kai Feng Yu Fu Jingjing Wang Lizeng Mao Beijing Key Laboratory of traffic Engineering Beijing University of TechnologyBeijing 100124China China Resources Wanxiang Life Shijiazhuang Wanxiang City Shijiazhuang 050031HebeiChina Beijing Municipal Transportation Operations Coordination Center Beijing 100073China

The exhaust emissions and frequent traffic incidents caused by traffic congestion have affected the operation and development of urban transport systems.Monitoring and accurately forecasting urban traffic operation is a critical task to formulate pertinent strategies to alleviate traffic congestion.Compared with traditional short-time traffic prediction,this study proposes a machine learning algorithm-based traffic forecasting model for daily-level peak hour traffic operation status prediction by using abundant historical data of urban traffic performance index(TPI).The study also constructed a multi-dimensional influencing factor set to further investigate the relationship between different factors on the quality of road network operation,including day of week,time period,public holiday,car usage restriction policy,special events,etc.Based on long-term historical TPI data,this research proposed a daily dimensional road network TPI prediction model by using an extreme gradient boosting algorithm(XGBoost).The model validation results show that the model prediction accuracy can reach higher than 90%.Compared with other prediction models,including Bayesian Ridge,Linear Regression,ElatsicNet,SVR,the XGBoost model has a better performance,and proves its superiority in large high-dimensional data sets.The daily dimensional prediction model proposed in this paper has an important application value for predicting traffic status and improving the operation quality of urban road networks.

关键词： traffic prediction traffic performance index(TPI) Influencing factor XGBOOST Machine learning model

Multi-Head Attention Spatial-Temporal Graph Neural Networks for traffic Forecasting

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Journal of Computer and Communications 2024年第3期12卷 52-67页

作者： Xiuwei Hu Enlong Yu Xiaoyu Zhao School of Computer Science and Technology Shandong University of Technology Zibo China Shandong Deyun Land Real Estate Appraisal Consulting Co. Zibo China College of Computer and Control Engineering Qiqihar University Qiqihar China

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.

关键词： traffic prediction Intelligent traffic System Multi-Head Attention Graph Neural Networks

prediction based traffic management in a metropolitan area

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Journal of traffic and Transportation Engineering(English Edition) 2020年第4期7卷 447-466页

作者： Suresh Chauhan Pallapa Venkataram Automotiue Research Center Vellore Institute of TechnologyVellore 632014India Department of Electrical Communication Engineering Indian Institute of ScienceBangalore 560012India

In recent years,modern metropolitan areas are the main indicators of economic growth of nation.In metropolitan areas,number and frequency of vehicles have increased tremendously,and they create issues,like traffic congestion,accidents,environmental pollution,economical losses and unnecessary waste of fuel.In this paper,we propose traffic management system based on the prediction information to reduce the above mentioned issues in a metropolitan area.The proposed traffic management system makes use of static and mobile agents,where the static agent available at region creates and dispatches mobile agents to zones in a metropolitan area.The migrated mobile agents use emergent intelligence technique to collect and share traffic flow parameters(speed and density),historical data,resource information,spatio-temporal data and so on,and are analyzes the static agent.The emergent intelligence technique at static agent uses analyzed,historical and spatio-temporal data for monitoring and predicting the expected patterns of traffic density(commuters and vehicles)and travel times in each zone and region.The static agent optimizes predicted and analyzed data for choosing optimal routes to divert the traffic,in order to ensure smooth traffic flow and reduce frequency of occurrence of traffic congestion,reduce traffic density and travel time.The performance analysis is performed in realistic scenario by integrating NS2,SUMO,OpenStreatMap(OSM)and MOVE tool.The effectiveness of the proposed approach has been compared with the existing approach.

关键词： traffic management traffic prediction Travel time prediction Agent Emergent intelligence Swarm intelligence

Double LSTM Structure for Network traffic Flow prediction

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国际计算机前沿大会会议论文集 2020年第1期 380-388页

作者： Lin Huang Diangang Wang Xiao Liu Yongning Zhuo Yong Zeng State Grid Sichuan Information and Communication Company Chengdu 610041China University of Electronic Science and Technology of China Chengdu 611731China The 10th Research Institute of China Electronics Technology Group Corporation Chengdu 610036China

The network traffic prediction is important for service quality control in computer network.The performance of the traditional prediction method significantly degrades for the burst short-term flow.In view of the problem,this paper proposes a double LSTMs structure,one of which acts as the main flow predictor,another as the detector of the time the burst flow starts at.The two LSTM units can exchange information about their internal states,and the predictor uses the detector’s information to improve the accuracy of the prediction.A training algorithm is developed specially to train the structure offline.To obtain the prediction online,a pulse series is used as a simulant of the burst event.A simulation experiment is designed to test performance of the predictor.The results of the experiment show that the prediction accuracy of the double LSTM structure is significantly improved,compared with the traditional single LSTM structure.

关键词： Time sequence Long-short term memory neural network traffic prediction Service quality control