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Geometric Algebra Multi-order Graph Neural Network for Traffic Prediction

IEEE Transactions on Big Data 2024年第3期11卷 1-15页

作者： Zang, Di Cui, Zhe Wang, Zengqiang Lei, Juntao Ding, Yongjie Wei, Chenguang Zhang, Junqi Department of Computer Science and Technology and the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Accurate traffic prediction is crucial for urban traffic management. Spatial-temporal graph neural networks, which combine graph neural networks with time series processing, have been extensively employed in traffic prediction. However, traditional graph neural networks only capture pairwise spatial relationships between road network nodes, neglecting high-order interactions among multiple nodes. Meanwhile, most work for extracting temporal dependencies suffers from implicit modeling and overlooks the internal and external dependencies of time series. To address these challenges, we propose a Geometric Algebraic Multi-order Graph Neural Network (GA-MGNN). Specifically, in the temporal dimension, we design a convolution kernel based on the rotation matrix of geometric algebra, which not only learns internal dependencies between different time steps in time series but also external dependencies between time series and convolution kernels. In the spatial dimension, we construct a tokenized hypergraph and integrate dynamic graph convolution with attention hypergraph convolution to comprehensively capture multi-order spatial dependencies. Additionally, we design a segmented loss function based on traffic periodic information to further improve prediction accuracy. Extensive experiments on seven real-world datasets demonstrate that GAMGNN outperforms state-of-the-art baselines IEEE

关键词： Graph neural networks

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A Prediction Based on Clustering and Personality Questionnaire Data for IGD Risk

A Prediction Based on Clustering and Personality Questionnai...

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International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery

作者： Xiaoliang Gong Kun Fang Yichu Hou Bozhong Long Zongling Di Lei Cao National Key Laboratory of Embedded System and Service Computing (Ministry of Education) Tongji University Shanghai China

ISBN: (纸本)9781509040940

With the development of the Internet, a lot of people trapped in the network, especially the adolescent depending on the network game and disturbing their normal life. 579 freshmen participated in this work who were collected the personality questionnaires in the first week they came in university and their grades points average (GPA) after half year. The questionnaires were including Self-Control (SCS), Barratt impulse Inventory (BIS) and Chinese Big Five Personality (CBF). This work used multi-clustering algorithms to construct the models of predicting for Internet game disorder (IGD) risk, including FCM, K-means, and Hierarchical for training model. This is the first try to predict the risk of IGD by personality traits. The results shown the questionnaire data were well separated by different clustering algorithms into three groups who were shared the analogous personality traits which has a relationship with the behavior of IGD. But compared to the GPA of each group, the efficiency of the prediction model seems not so satisfactory. There need more efforts to optimized the model in the future.

关键词： Personality questionnaire Data IGD Risk FCM K-means Hierarchical

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ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting

arXiv

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arXiv 2024年

作者： Zheng, Qi Yao, Zihao Zhang, Yaying The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China

Spatial-temporal forecasting is crucial and widely applicable in various domains such as traffic, energy, and climate. Benefiting from the abundance of unlabeled spatial-temporal data, self-supervised methods are increasingly adapted to learn spatial-temporal representations. However, it encounters three key challenges: 1) the difficulty in selecting reliable negative pairs due to the homogeneity of variables, hindering contrastive learning methods;2) overlooking spatial correlations across variables over time;3) limitations of efficiency and scalability in existing self-supervised learning methods. To tackle these, we propose a lightweight representation-learning model ST-ReP, integrating current value reconstruction and future value prediction into the pre-training framework for spatial-temporal forecasting. And we design a new spatial-temporal encoder to model fine-grained relationships. Moreover, multi-time scale analysis is incorporated into the self-supervised loss to enhance predictive capability. Experimental results across diverse domains demonstrate that the proposed model surpasses pre-training-based baselines, showcasing its ability to learn compact and semantically enriched representations while exhibiting superior scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Study and implementation of agent-based grid resource monitoring model

Study and implementation of agent-based grid resource monito...

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2nd International Conference on Information Science and Engineering, ICISE2010

作者： Ming, Liu Zhang, Junqi Wang, Yu Li, Jie Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

ISBN: (纸本)9781424480968

Grid is a promising infrastructure which enables scientists and engineers to access geographically distributed resources. Grid computing is a new technology which focuses on aggregating various kinds of resource (e.g., processor cycles, disk storage, and contents) into one computing platform. The realization of grid computing requires a resource agent to manage and monitor available resources. Based on study of past models, this paper presents a new agent-based resource monitoring model whose main feature is multi-layered monitoring architecture, which enhances the ability to monitor related resource efficiently. In the meantime, the new model has been implemented in the development of a resource monitoring module integrated within a academic grid project. © 2010 IEEE.

关键词： Grid computing

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A Remote Debugging system for OpenMP Parallel Program

A Remote Debugging System for OpenMP Parallel Program

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International Conference on Transportation, Mechanical, and Electrical Engineering

作者： Hua Chai Qinqin Shi The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

ISBN: (纸本)9781457717000

A remote debugging system for OpenMP parallel program is presented in this paper. The system consists of two parts, namely, an integrated debugging environment running on the clent-side and a background daemon running on the server-side. Information exchange between the two sides is accomplished through the socket-based network communication technology. The remote debugging function is realized by the automatic instrumentation technology at code level based on syntax tree. Instance tests show that the system can provide remote users with correctness checking, performance analysis and other debugging functions for OpenMP parallel programs. The user interface is simple and very easy to use.

关键词： Parallel Program OpenMP Remote Debugging Syntax Tree Instrumentation Technology

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Study on stability of traffic flow for cooperative driving control

Study on stability of traffic flow for cooperative driving c...

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IEEE Symposium on Intelligent Vehicle

作者： Zhipeng Li Ping Wang Fuqiang Liu Key Laboratory of Embedded System and ServIce Computing supported by Ministry of Education University of Tongji Shanghai China

ISBN: (纸本)9781424435036

In the literature of traffic flow theory, the research on the effect on stability of traffic flow for cooperative driving control possesses an important significance. However, presently the the study on the problem is unsatisfactory because it is difficult to determine the impact qualitatively or quantitatively in real traffic experiment. In this paper, some efforts have been made for better understanding the effect on stability of traffic flow for cooperative driving control by investigating the stability for lattice traffic models, which are presented here by incorporating motion information of cars preceding. From linear stability analysis and direct simulations validation, we learn some properties of the effect on the stability and congestion waves by using the information of many other cars. First, cooperative driving behavior of many cars preceding can efficiently stabilize the traffic flow. Second, cooperative driving behavior of the cars nearby plays a prominent role in stability. Third, when the car number participating in cooperative driving policy exceeds a certain value, the congestion waves will disappear.

关键词： Traffic control Control systems Communication system traffic control Roads Laboratories embedded system embedded computing Stability analysis Transportation Mathematical model

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Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting

arXiv

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arXiv 2022年

作者： Liu, Aoyu Zhang, Yaying The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

Accurate traffic forecasting is essential for urban traffic control, route planning, and flow detection. Although many spatial-temporal methods are currently proposed, they are still deficient in synchronously capturing the spatial-temporal dependence of traffic data. In addition, most methods ignore the hidden dynamic associations that arise between the road network nodes as it evolves over time. We propose a neural network-based Spatial-Temporal Interactive Dynamic Graph Convolutional Network (STIDGCN) to address the above challenges for traffic forecasting. Specifically, we propose an interactive dynamic graph convolution structure which divides the traffic data by intervals and synchronously captures the divided traffic data‘s spatial-temporal dependence through an interactive learning strategy. The interactive learning strategy motivates STIDGCN effective for long-range forecasting. We also propose a dynamic graph convolution module through a novel dynamic graph generation method to capture the dynamically changing spatial correlations in the traffic network. Based on a priori knowledge and input data, the dynamic graph generation method can generate a dynamic graph structure, which allows exploring the unseen node connections in the road network and simulating the dynamic associations between nodes over time. Extensive experiments on four real-world traffic flow datasets demonstrate that STIDGCN outperforms the state-of-the-art baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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Reliable Web services Selection Using a Heuristic Algorithm

Reliable Web Services Selection Using a Heuristic Algorithm

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The Ninth International Conference on Grid and Cloud computing(第九届网格与云计算国际学术会议 GCC 2010)

作者： Hongjie Shen Zhijun Ding Hongzhong Chen The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

Web services composition (WSC) is the key techniques in its application. Dynamically selecting reliable Web services (WSs) becomes crucial to users. In fact, most works regard a Web service (WS) as the basic unit and selection with end-to-end, and as more and more WSs become available, the WSs selection problem is a complex NP-hard optimization problem. So, in this paper, the WSs consist of the operations and the WSs selection problem is formalized as a finite state machine process and we proposed a backward algorithm to create the WSC tree (WSCT). Then, we proposed a heuristic algorithm to complete the selection of WSs based on the WSCT. The results of experiment show our proposed heuristic algorithm is effective.

关键词： Web services composition heuristic algorithm service selection reliability

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A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

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IEEE/CAA Journal of Automatica Sinica 2018年第5期5卷 934-945页

作者： Liang Qi Mengchu Zhou Wenjing Luan IEEE Department of Computer Scicnce and Technology Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102USA Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghai Electronic Transactions and Information Service Collaborative Innovation CenterDepartment of Com-puter ScienceTongji UniversityShanghai 201804China

Advanced information and communication technolo-gies can be used to facilitate traffic incident *** an incident is detected and blocks a road link,in order to reduce the incident-induced traffic congestion,a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this ***-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information.A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well *** results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles,operation time interval of the proposed strategy,traffic density in the traffic network,and the scope of the area where traffic incident information is *** results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

关键词： Cell transmission model(CTM) intelligent transportation systems(ITS) traffic incident management(TIM) urban traffic congestion

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A survey on distributed compressed sensing： theory and applications

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Frontiers of Computer Science 2014年第6期8卷 893-904页

作者： Hongpeng YIN Jinxing LI Yi CHAI Simon X. YANG College of Automation Chongqing University Chongqing 400030 China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing 400030 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing 400030 China School of Engineering University of Guelph Guelph N1G 2W1 Canada

The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.

关键词： compressed sensing distributed compressed sensing sparse representation measurement matrix joint reconstruction joint sparsity model

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