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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年

作者： Wang, Shuang Zhang, Linhan Pan, Yuezheng Chen, Haoyu Pang, Zhenlong Northeastern Univ Software Coll Shenyang 110167 Peoples R China

Trajectory similarity computing enables us to gain deeper insights into the movement patterns of objects, which benefits intelligent transportation system applications from urban planning to travel recommendation. In this study, we explore a new spatial-temporal Graph Contrastive Learning method for Trajectory Similarity computation (STGCL-TrajSim). We design two contrastive learning methods on the road network trajectory. First, time contrastive learning employs two data augmentation methods and efficient sampling strategies to capture multi-scale temporal information. Secondly, spatial contrastive learning adopts an importance-based graph augmentation strategy to preserve both the graph topology and spatial structure information. Thirdly, we use the road transition probability matrix to represent the road access frequency, and adopt a time interval matrix to reflect traffic congestion conditions, and design a co-attention mechanism to capture the interactions between temporal and spatial features. Finally, experimental results on real datasets show that our model outperforms state-of-the-art competitors consistently and achieves better performance in terms of accuracy.

关键词： Trajectory Roads Contrastive learning Semantics Vectors Accuracy Topology Noise Learning systems Global Positioning System Trajectory similarity spatial-temporal data analysis contrastive learning road network

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Urban regional function guided traffic flow prediction

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INFORMATION SCIENCES 2023年第1期634卷 308-320页

作者： Wang, Kuo Liu, LingBo Liu, Yang Li, GuanBin Zhou, Fan Lin, Liang Sun Yat sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest. In addition to spatial-temporal correlations, the functionality of urban areas also plays a crucial role in traffic flow prediction. However, the exploration of regional functional attributes mainly focuses on adding additional topological structures, ignoring the influence of functional attributes on regional traffic patterns. Different from the existing works, we propose a novel module named POI-MetaBlock, which utilizes the functionality of each region (represented by Point of Interest distribution) as metadata to further mine different traffic characteristics in areas with different functions. Specifically, the proposed POI-MetaBlock employs a self-attention architecture and incorporates POI and time information to generate dynamic attention parameters for each region, which enables the model to fit different traffic patterns of various areas at different times. Furthermore, our lightweight POI-MetaBlock can be easily integrated into conventional traffic flow prediction models. Extensive experiments demonstrate that our module significantly improves the performance of traffic flow prediction and outperforms state-of-the-art methods that use metadata.

关键词： Traffic flow prediction datasets Graph neural networks data mining spatial-temporal data analysis

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Predicting City Origin-Destination Flow with Generative Pre-training 33rd

Predicting City Origin-Destination Flow with Generative Pre-...

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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Zhang, Mingwei Gao, Lizhong Wang, Qiao Gao, Weihao Southeast Univ Nanjing Peoples R China Tsinghua Univ Tsinghua Int Grad Sch Beijing Peoples R China

ISBN: (纸本)9783031723551;9783031723568

Predicting Origin-Destination (OD) flow is a critical issue in the construction and management of smart cities, with challenges mainly stemming from the complex spatial-temporal dependencies in urban environments for three reasons: first, a considerable number of city grids are interrelated;second, the diversity of travel modes leads to highly imbalanced feature distributions in crowd flow data;lastly, crowd flow and geographical indicators have potential correlations. Inspired by natural language processing, we apply the concept of Generative Pre-training (GPT) to OD flow prediction. The proposed OD-GPT model frames grid sequence prediction as next token prediction in language models. Extensive testing on large-scale mobile signaling datasets from two real-world cities demonstrates the effectiveness of the model. In comparison to baseline models, it exhibits a notable enhancement in one-step prediction accuracy. Furthermore, we explore the model's multi-step prediction capability and research its adaptability to unconventional day through fine-tuning. The analysis indicates that our model can extract latent geographical features from OD data and generate reasonable embeddings with precise dynamic spatial-temporal dependencies.

关键词： Origin-Destination flow prediction generative pre-training grid embedding spatial-temporal data analysis

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A Cloud-Edge Computing Approach for Epidemic Close Contact Tracing Based on spatial-temporal Trajectory 7

A Cloud-Edge Computing Approach for Epidemic Close Contact T...

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IEEE 7th International Workshop on Radio Frequency and Antenna Technologies (IEEE IWRF and AT)

作者： Yue, Shaohua Xu, Zhuocheng Di, Boya Peking Univ Sch Elect Beijing Peoples R China Peking Univ Shenzhen Grad Sch Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Frontier Res Ctr Shenzhen Peoples R China

ISBN: (纸本)9798350384765;9798350384772

Contact tracing, as an effective method to slow the spread of epidemics, is traditionally performed with manual surveys on positive cases to acquire information about close contacts, which induces an unbearably long tracing duration. In this paper, we propose a cloud-edge computing-aided contact tracing system where multi-access edge computing (MEC) servers are introduced to collaborate with the cloud center (CC) to mitigate the duration issue. Collecting the spatial-temporal data of mobile users, the MEC-integrated base stations organize these data via the R-tree structure for efficient tracing task processing. After aggregating spatial-temporal data from MEC servers, the CC partitions and assigns contact tracing tasks to different servers for parallel computing. An iterative transmission bandwidth allocation and task assignment algorithm is designed to minimize the overall tracing duration, where the correlation of the cases' positions and MEC servers as well as the limited buffer size of MEC servers are considered. Simulation results show that with the proposed scheme, the tracing duration is reduced by 55.8% compared with cloud computing, and a 100% tracing accuracy is achieved.

关键词： Cloud-edge computing epidemic contact tracing spatial-temporal data analysis

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TCGL: temporal Contrastive Graph for Self-Supervised Video Representation Learning

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 1978-1993页

作者： Liu, Yang Wang, Keze Liu, Lingbo Lan, Haoyuan Lin, Liang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Video self-supervised learning is a challenging task, which requires significant expressive power from the model to leverage rich spatial-temporal knowledge and generate effective supervisory signals from large amounts of unlabeled videos. However, existing methods fail to increase the temporal diversity of unlabeled videos and ignore elaborately modeling multi-scale temporal dependencies in an explicit way. To overcome these limitations, we take advantage of the multi-scale temporal dependencies within videos and propose a novel video self-supervised learning framework named temporal Contrastive Graph Learning (TCGL), which jointly models the inter-snippet and intra-snippet temporal dependencies for temporal representation learning with a hybrid graph contrastive learning strategy. Specifically, a spatial-temporal Knowledge Discovering (STKD) module is first introduced to extract motion-enhanced spatial-temporal representations from videos based on the frequency domain analysis of discrete cosine transform. To explicitly model multi-scale temporal dependencies of unlabeled videos, our TCGL integrates the prior knowledge about the frame and snippet orders into graph structures, i.e., the intra-/inter-snippet temporal Contrastive Graphs (TCG). Then, specific contrastive learning modules are designed to maximize the agreement between nodes in different graph views. To generate supervisory signals for unlabeled videos, we introduce an Adaptive Snippet Order Prediction (ASOP) module which leverages the relational knowledge among video snippets to learn the global context representation and recalibrate the channel-wise features adaptively. Experimental results demonstrate the superiority of our TCGL over the state-of-the-art methods on large-scale action recognition and video retrieval benchmarks. The code is publicly available at https://***/YangLiu9208/TCGL.

关键词： Task analysis Representation learning Discrete cosine transforms Legged locomotion Feature extraction Supervised learning Streaming media Video understanding self-supervised learning graph neural networks spatial-temporal data analysis

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Location prediction with personalized federated learning

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SOFT COMPUTING 2022年

作者： Wang, Shuang Wang, Bowei Yao, Shuai Qu, Jiangqin Pan, Yuezheng Northeastern Univ Shenyang Shenyang Peoples R China

Location prediction has attracted wide attention in human mobility prediction because of the popularity of location-based social networks. Existing location prediction methods have achieved remarkable development in centrally stored datasets. However, these datasets contain privacy data about user behaviors and may cause privacy issues. A location prediction method is proposed in our work to predict human movement behavior using federated learning techniques in which the data are stored in different clients and different clients cooperate to train to extract useful users' behavior information and prevent the disclosure of privacy information. Firstly, we put forward an innovative spatiotemporal location prediction framework (STLPF) for location prediction by integrating spatiotemporal information in local and global views on each client and propose a new loss function to optimize the model. Secondly, we design a new personalized federated learning framework in which clients can cooperatively train their personalized models in the absence of a global model. Finally, the numerous experimental results on check-in datasets further show that our privacy-protected method is superior and more effective than various baseline approaches.

关键词： Location prediction Self-attention Federated learning spatial-temporal data analysis

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Knowledge Graphs in spatial-temporal Cluster Evolution analysis

Knowledge Graphs in Spatial-Temporal Cluster Evolution Analy...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Portugal, Ivens Alencar, Paulo Cowan, Donald University of Waterloo School of Computer Science Waterloo Canada

ISBN: (纸本)9798350324457

Graphs have been used as the foundation for several types of analysis methods in multiple application domains. In our study, we investigate spatial-temporal data analysis using graphs that capture knowledge about clusters of moving objects, their relationships, and their evolution. The study cope with the need to provide analysis techniques that consider cluster evolution. In this paper, we describe our ongoing study on knowledge graphs in spatial-temporal cluster evolution and potential research directions. © 2023 IEEE.

关键词： cluster evolution knowledge graph spatial-temporal data analysis

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JS-STDGN: A spatial-temporal Dynamic Graph Network Using JS-Graph for Traffic Prediction 27th

JS-STDGN: A Spatial-Temporal Dynamic Graph Network Using JS-...

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27th International Conference on database Systems for Advanced Applications (DASFAA)

作者： Li, Pengfei Fang, Junhua Chao, Pingfu Zhao, Pengpeng Liu, An Zhao, Lei Soochow Univ Dept Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9783031001239;9783031001222

Traffic prediction is a fundamental operation in real-time traffic analysis. A precise prediction of traffic condition can benefit both road users and traffic management agencies. However, since road traffic is decided by multiple static and dynamic factors, traffic prediction is still a challenging task. As the core indicator of traffic condition, many works focus on traffic speed prediction using time-series forecasting approaches. Although current methods take into account the static road topology while modelling, they fail to consider (1) the semantic closeness between road components and (2) congestion caused by upstream/downstream traffic propagation. In this paper, we introduce a spatial-temporal Dynamic Graph Network using JS-Graph, which considers both static road features and dynamic traffic flows when forecasting. Specifically, we first propose a data-driven 'JS-Graph' method that describes the semantic similarity between road nodes. It models the complex spatial correlations that cannot be captured by the traditional spatial adjacency graph. Secondly, we design a dynamic graph attention network that considers the traffic dynamics that happened in previous time slices when predicting the current one to capture the congestion propagation phenomena. Extensive experiments conducted on real-world datasets show that our proposed method is significantly better than baselines.

关键词： Graph neural network spatial-temporal data analysis Time series forecast

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Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network 36

Predicting Origin-Destination Flow via Multi-Perspective Gra...

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IEEE 36th International Conference on data Engineering (ICDE)

作者： Shi, Hongzhi Yao, Quanming Guo, Qi Li, Yaguang Zhang, Lingyu Ye, Jieping Li, Yong Liu, Yan Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Elect Engn Beijing 100084 Peoples R China 4Paradigm Inc Hong Kong Peoples R China Sichuan Univ Coll Comp Sci Chengdu Peoples R China Google Inc Mountain View CA USA Didi Chuxing AI Labs Beijing Peoples R China Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA

ISBN: (纸本)9781728129037

Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation. However, it is extremely challenging because of three reasons: first, correlations exist. between both origins and destinations;second, the correlations are dynamic across the time;at last, there are multiple correlations from different aspects. To the best of our knowledge, existing models for OD flow prediction cannot tackle all of these three issues simultaneously. We propose Multi -Perspective Graph Convolutional Networks (MPGCN) to capture the complex dependencies. Our proposed model first utilizes long short-term memory (LSTM) network to extract temporal features for each OD pair and then learns the spatial dependency of origins and destinations by a two-dimensional graph convolutional network. Furthermore, we design a dynamic graph together with two static graphs to capture the complicated spatial dependencies and use an average strategy to obtain the final predicted OD flow. We conduct extensive experiments on two large-scale and real-world datasets, which not only demonstrate our design philosophy but also validate the effectiveness of the proposed model.

关键词： Origin-destination traffic prediction graph convolutional network spatial-temporal data analysis

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A Framework for spatial-temporal Trajectory Cluster analysis Based on Dynamic Relationships

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IEEE ACCESS 2020年 8卷 169775-169793页

作者： Portugal, Ivens Alencar, Paulo Cowan, Donald Univ Waterloo Sch Comp Sci Waterloo ON N2L 3G1 Canada

In spatial-temporal data analysis, location data and its evolution through time are investigated with the goal of uncovering important information to provide novel insights. These insights, for example, may involve congestion identification in transportation, mobility patterns in urban computing, and storm prediction in weather forecasting. Clustering, one data analysis technique, groups spatial-temporal data based on location. Current spatial-temporal data analysis techniques fail to investigate relationships between spatial-temporal clusters, such as splitting from a cluster and merging with another one because of a change of properties over time. These relationships could hold valuable information about the existence of a cluster and its interactions with other clusters and trajectories. In this paper, we introduce a framework to identify, process, and analyze relationships between clusters of spatial-temporal data (e.g. enter, merge, or split). We describe its architecture and components, as well as a proposed clustering technique, the different approaches for distance calculation, and how we calculate cluster similarity of temporally separated clusters. The result of these operations are used in the identification of cluster relationships over space and time. The analysis of these relationships helps uncover hidden values that could support novel approaches to more effective decision-making. We evaluate our framework with two case studies, based on truck and human trajectories.

关键词： data analysis Clustering algorithms Anomaly detection Trajectory Task analysis data models Computer science Cluster analysis spatial-temporal cluster relationships spatial-temporal data spatial-temporal data analysis

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