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A novel physics-guided spatial-temporal data mining method with external and internal causal attention for drilling risk evaluation

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2024年 42卷

作者： Qu, Fengtao Liao, Hualin Wang, Huajian Liu, Jiansheng Wu, Tianyu Xu, Yuqiang China Univ Petr East China Sch Petr Engn Qingdao 266580 Shandong Peoples R China MATRIX Corp SINOPEC Qingdao 266071 Shandong Peoples R China

As drilling technology advances and operations extend into more complex geological environments, evaluating drilling risks has become increasingly complex, challenging the effectiveness of traditional methods. The novel physics-guided spatial-temporal data mining method that integrates external and internal causal attention mechanisms for drilling risk evaluation is proposed to address this issue. Firstly, a risk calibration method based on spatial-temporal sequence clustering is designed. This method dynamically calibrates drilling risks by mining subtle changes in sign data during drilling. Secondly, an expert experience extraction method based on the correlation of drilling risk signs and a fuzzy inference system is established. Kendall's tau is used to quantify the correlation between drilling risk signs. The fuzzy inference system is employed to convert fuzzy and difficult-toquantify expert experience into computable and interpretable rules. In order to improve the flexibility and adaptability of the fuzzy inference system, an expert experience rules base is also constructed. Subsequently, a spatial-temporal data mining model integrating both external and internal causal attention mechanisms (STMIEICAM) is constructed. The external causal attention mechanism (ECAM) quantified the correlation between signs and risk. The internal causal attention mechanism (ICAM) improved the model's ability to capture and quantify the features of spatial-temporal sequences. Finally, the physical knowledge from the fuzzy inference system and well-site is embedded into the STMIEICAM model, forming a physics-guided spatial-temporal data mining model integrating both external and internal causal attention mechanisms (PG-STMIEICAM) that enables graded evaluation of drilling risks. The proposed method was applied to overflow risk evaluation in an oil field to validate its effectiveness. The results demonstrate that the method not only excels in uncovering hidden relationships within t

关键词： Drilling risk evaluation Physics-guided model spatial-temporal data mining Fuzzy inference system Causal attention mechanism

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Cross-Transportation-Mode Knowledge Transfer for Trajectory Recovery With Meta Learning

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Wang, Chenxing Zhao, Fang Luo, Haiyong Sun, Poly Z. H. Fang, Yuchen Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch Beijing 100876 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing 100080 Peoples R China East China Normal Univ Sch Psychol & Cognit Sci Shanghai 200062 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Sichuan Peoples R China

Transportation mode-aware trajectory recovery is the fundamental for individual oriented downstream tasks in intelligent transportation systems. Different from vehicle based trajectory recovery, it suffers from the heterogeneity and sparsity issues arising from the insufficient data labelled for distinct transportation modes (e.g., obtaining limited individual trajectories from modes like walking or cycling due to privacy concerns while obtaining rich vehicle trajectories from the mode like driving). To alleviate this, we develop a novel Cross-trAnsportation-mode Knowledge transfEr method with meta learning, coined as Cake, to first learn generalized parameters from source modes (i.e., the relatively dense modes) and then share the meta knowledge with the targets (i.e., more sparse modes), which significantly improve the recovery performance for the sparse. To achieve this, we first develop an efficient fine-GRAined Personalized trajectory rEcovery model called Grape, to incorporate the cross-granularity features with coarse-centered and fine-centered subgraph learning and learn the intrinsic characteristics of transportation modes with auto-correlation efficiently. Then we design a personalized memory to store distinct parameters for diverse interests of individual groups and read the memory for predictor's input features according to the previous learnt features. At last, we employ the feature reuse strategy based on meta learning to iteratively make adaptions from the source to the target. Extensive experimental results on real-world dataset demonstrate that our proposed method significantly outperforms the state-of-arts for the sparse modes.

关键词： Trajectory Transportation Roads Metalearning Legged locomotion Knowledge transfer Transformers Global Positioning System Video recording Predictive models Transportation mode-aware trajectory recovery spatial-temporal data mining knowledge transfer

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ConDTC: Contrastive Deep Trajectory Clustering for Fine-Grained Mobility Pattern mining

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IEEE TRANSACTIONS ON BIG data 2025年第2期11卷 333-344页

作者： Si, Junjun Yang, Jin Xiang, Yang Li, Li Tu, Bo Zhang, Rongqing Commun Univ China Sch Comp & Cyber Sci Beijing 100024 Peoples R China Hezhixin Shandong Big Data Technol Co Ltd Jinan 250001 Peoples R China Beijing Informat Sci & Technol Univ Beijing 100192 Peoples R China Tongji Univ Sch Software Engn Shanghai 200070 Peoples R China

Trajectory clustering is a cornerstone task in the field of trajectory mining. With the proliferation of deep learning, deep trajectory clustering has been widely researched to mine mobility patterns from massive unlabeled trajectories. Nevertheless, existing methods mostly ignore trajectories' temporal regularities, which are essential for mining fine-grained mobility patterns for applications including traveling group identification, transportation mode discovering, social security emergency, etc. To fill this gap, we propose ConDTC, a contrastive deep trajectory clustering method targeting for fine-grained mobility pattern mining. Specifically, we first design a spatial-temporal trajectory representation learning method which can capture both spatial and temporal regularities of trajectories synchronously. The proposed trajectory representation model can be used as a pre-trained model to serve various downstream trajectory mining tasks. Then, we construct a contrastive trajectory clustering module which optimizes trajectory representations and clustering performance simultaneously. Experimental results on three datasets validate that ConDTC can identify fine-grained mobility patterns by clustering trajectories with similar spatial-temporal mobility patterns together while separating those with different mobility patterns apart. Actually, ConDTC outperforms all state-of-the-art competitors substantially in terms of effectiveness, efficiency and robustness.

关键词： Trajectory Clustering methods Representation learning data mining Task analysis Self-supervised learning Big data Trajectory clustering trajectory representation learning trajectory mining spatial-temporal data mining

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RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning

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Proceedings of the ACM on Management of data 2025年第1期3卷 1-26页

作者： Zhihan Zheng Haitao Yuan Minxiao Chen Shangguang Wang Beijing University of Posts and Telecommunications Beijing China Nanyang Technological University Singapore Singapore

En Route Travel Time Estimation (ER-TTE) aims to learn driving patterns from traveled routes to achieve rapid and accurate real-time predictions. However, existing methods ignore the complexity and dynamism of real-world traffic systems, resulting in significant gaps in efficiency and accuracy in real-time scenarios. Addressing this issue is a critical yet challenging task. This paper proposes a novel framework that redefines the implementation path of ER-TTE to achieve highly efficient and effective predictions. Firstly, we introduce a novel pipeline consisting of a Decision Maker and a Predictor to rectify the inefficient prediction strategies of current methods. The Decision Maker performs efficient real-time decisions to determine whether the high-complexity prediction model in the Predictor needs to be invoked, and the Predictor recalculates the travel time or infers from historical prediction results based on these decisions. Next, to tackle the dynamic and uncertain real-time scenarios, we model the online decision-making problem as a Markov decision process and design an intelligent agent based on reinforcement learning for autonomous decision-making. Moreover, to fully exploit the spatio-temporal correlation between online data and offline data, we meticulously design feature representation and encoding techniques based on the attention mechanism. Finally, to improve the flawed training and evaluation strategies of existing methods, we propose an end-to-end training and evaluation approach, incorporating curriculum learning strategies to manage spatio-temporal data for more advanced training algorithms. Extensive evaluations on three real-world datasets confirm that our method significantly outperforms state-of-the-art solutions in both accuracy and efficiency.

关键词： road networks spatial-temporal data mining trajectory travel time estimation

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MFDS-STGCN: Predicting the Behaviors of College Students With Fine-Grained spatial-temporal Activities data

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2024年第1期12卷 254-265页

作者： Zhou, Dongbo Yu, Hongwei Yu, Jie Zhao, Shuai Xu, Wenhui Li, Qianqian Cai, Fengyin Cent China Normal Univ Fac Artificial Intelligence Educ Wuhan 430079 Peoples R China Beihang Univ Coll Software Beijing 100191 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Wuhan 430072 Peoples R China

mining and predicting college students behaviors from fine-grained spatial-temporal campus activity data play key roles in the academic success and personal development of college students. Most of the existing behavior prediction methods use shallow learning algorithms such as statistics, clustering, and correlation analysis approaches, which fail to mine the long-term spatial-temporal dependencies and semantic correlations from these fine-grained campus data. We propose a novel multi-fragment dynamic semantic spatial-temporal graph convolution network, named the MFDS-STGCN, on the basis of a spatial-temporal graph convolutional network (STGCN) for the automatic prediction of college students' behaviors and abnormal behaviors. We construct a dataset including 7.6 million behavioral records derived from approximately 400 students over 140 days to evaluate the effectiveness of the prediction model. Extensive experimental results demonstrate that the proposed method outperforms multiple baseline prediction methods in terms of student behavior prediction and abnormal behavior prediction, with accuracies of 92.60% and 90.84%, respectively. To further enable behavior prediction, we establish an early warning management mechanism. Based on the predictions and analyses of Big data, education administrators can detect undesirable abnormal behaviors in time and thus implement effective interventions to better guide students' campus lives, ultimately helping them to more effectively develop and grow.

关键词： Abnormal behavior prediction dynamic semantic graphs Early behavior warning management graph convolutional neural network spatial-temporal data mining

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Inductive and adaptive graph convolution networks equipped with constraint task for spatial-temporal traffic data kriging

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KNOWLEDGE-BASED SYSTEMS 2024年 284卷

作者： Wei, Tonglong Lin, Youfang Guo, Shengnan Lin, Yan Zhao, Yiji Jin, Xiyuan Wu, Zhihao Wan, Huaiyu Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

In intelligent transportation systems (ITS), deploying fine-grained sensors to continuously collect spatial- temporal traffic data is important but impractical due to the expensive cost. Fortunately, spatial-temporal kriging methods bring advanced solutions for interpolating traffic data for locations without sensors, but they still have the following two drawbacks: (1) The widely adopted predefined and adaptive graphs are either inflexible or limited to transductive learning. (2) The sampling strategies to support inductive learning on new graphs result in losing important partial information. To overcome the above issues, in this paper, we propose an Inductive and Adaptive Graph Convolution Networks (IAGCN) for spatial-temporal traffic data kriging in an inductive manner. Specifically, we propose an adaptive graph constructor to model the hidden spatial relation of nodes and learn the spatial dependency that the predefined graph structure cannot capture. It can work inductively and does not require retraining when introducing new nodes. Additionally, we design a framework that integrates inductive graph convolution and temporal convolution to simultaneously capture the complex spatial-temporal dependencies of traffic data. Finally, to address the information loss issue caused by random sampling, we design a predicted-based constraint task that perceives and utilizes the history information of all observed sensors to predict the current data, as well as approach the result of the interpolation and prediction. Experiments on four real-world datasets show that IAGCN outperforms the state-of-the-art baselines.

关键词： Kriging spatial-temporal data mining Graph neural networks

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Multi-Stage Fusion Framework for Short-Term Passenger Flow Forecasting in Urban Rail Transit Systems Using Multi-Source data

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TRANSPORTATION RESEARCH RECORD 2024年第9期2678卷 18-36页

作者： Chen, Yijie Zhang, Jinlei Lu, Yuan Yang, Kuo Liu, Hanxiao Liang, Ying Beijing Jiaotong Univ Sch Syst Sci Beijing Peoples R China Beijing Jiaotong Univ Sch Architecture & Design Beijing Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Beijing Gen Municipal Engn Design & Res Inst Co Lt Beijing Peoples R China Xi An Rail Transit Grp Co Ltd Operat Branch Xian Peoples R China

To improve real-time operation and management in urban rail transit (URT) systems, accurate and reliable short-term passenger flow forecasting at the network level is a crucial task. Although numerous endeavors have been devoted to this field, the insufficient topological representation for passenger flows in the URT network, the overlooking of intrinsic correlations among multi-source data, and the information loss in deep-learning frameworks are still critical issues that need to be addressed. This study proposes a multi-stage fusion passenger forecasting (MSFPF) model to accomplish short-term multi-step passenger forecasting leveraging multi-source data, and overcome the above-mentioned challenges. Based on the characteristics of passenger flows in the URT network, time-based origin-destination flow data is involved and utilized to enhance the representation of flows and provide spatial-temporal features. Then, the interaction and relationship among multi-source data are estimated to capture their intrinsic correlations. To effectively and comprehensively extract temporal and spatial features, a transformer long short-term memory block and a depth-wise attention block are constructed with attention mechanisms and employed. Furthermore, we construct the multi-stage fusion (MSF) structure to alleviate the information loss during the learning process, which is a significant component in improving the forecasting accuracy. In addition, the model is applied to two large-scale real-world datasets, in which it outperforms nine widely used baselines and four specific variants of itself. The quantitative experiments demonstrate the robustness and superiority of the proposed MSFPF model, and the significant contribution of the MSF structure in the model.

关键词： urban rail transit system deep learning and advanced computing applications short-term forecasting spatial-temporal data mining multi-source data fusion

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Predicting ocean salinity and temperature variations using data mining and fuzzy inference

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2007年第3期9卷 143-151页

作者： Huang, Yo-Ping Kao, Li-Jen Sandnes, Frode-Eika Tatung Univ Dept Comp Sci & Engn Taipei 10451 Taiwan

Global ocean salinity/temperature variations are attracting increasing attention, due to their influence on ocean-atmospheric changes and their potential for improved climate forecasting. The goal is to analyze historic salinity/temperature data to make predictions about future variations. Traditional statistical models that assume data independence are not applicable as ocean data are often inter-related. Association rules mining can be used to find interesting salinity and temperature patterns, however, the traditional method ignores spatial and temporal information in the data. This study proposes a strategy that employs inter-transaction association rules mining to discover salinity/temperature patterns where spatial/temporal relationships are considered. Next, a fuzzy inference is used to predict salinity/temperature variations. The fuzzy inference rules are derived from a set of inter-transaction association rules that are discovered from Argo data. The strategy is highly efficient as a reduced prefix-projected itemset algorithm with a small space and time complexity is employed in the search for large inter-transaction itemsets. This proposed strategy is unsupervised as it does not rely on domain experts for designing the fuzzy rule base. Experimental results demonstrate that the proposed strategy effectively predicts abnormal salinity/temperature variations.

关键词： fuzzy inter-transaction association rule mining spatial-temporal data mining fuzzy inference climate change

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Towards Effective Fusion and Forecasting of Multimodal Spatio-temporal data for Smart Mobility 24

Towards Effective Fusion and Forecasting of Multimodal Spati...

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33rd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Wang, Chenxing Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9798400704369

With the rapid development of location based services, multimodal spatio-temporal (ST) data including trajectories, transportation modes, traffic flow and social check-ins are being collected for deep learning based methods. These deep learning based methods learn ST correlations to support the downstream tasks in the fields such as smart mobility, smart city and other intelligent transportation systems. Despite their effectiveness, ST data fusion and forecasting methods face practical challenges in real-world scenarios. First, forecasting performance for ST data-insufficient area is inferior, making it necessary to transfer meta knowledge from heterogeneous area to enhance the sparse representations. Second, it is nontrivial to accurately forecast in multi-transportation-mode scenarios due to the fine-grained ST features of similar transportation modes, making it necessary to distinguish and measure the ST correlations to alleviate the influence caused by entangled ST features. At last, partial data modalities (e.g., transportation mode) are lost due to privacy or technical issues in certain scenarios, making it necessary to effectively fuse the multimodal sparse ST features and enrich the ST representations. To tackle these challenges, our research work aim to develop effective fusion and forecasting methods for multimodal ST data in smart mobility scenario. In this paper, we will introduce our recent works that investigates the challenges in terms of various real-world applications and establish the open challenges in this field for future work.

关键词： spatial-temporal data mining Intelligent Transportation Systems Travel Time Estimation Transportation Mode Detection Trajectory Recovery

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A Distributed Framework for Online Stream data Clustering 20th

A Distributed Framework for Online Stream Data Clustering

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20th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Ding, Jiafeng Fang, Junhua Chao, Pingfu Xu, Jiajie Zhao, PengPeng Zhao, Lei Soochow Univ Dept Comp Sci & Technol Suzhou Peoples R China Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9783030602451;9783030602444

The recent prevalence of positioning sensors and mobile devices generates a massive amount of spatial-temporal data from moving objects in real-time. As one of the fundamental processes in data analysis, the clustering on spatial-temporal data creates various applications, like event detection and travel pattern extraction. However, most of the existing works only focus on the offline scenario, which is not applicable to online time-sensitive applications due to their low efficiency and ignorance of temporal features. In this paper, we propose a distributed streaming framework for spatial-temporal data clustering, which accepts various clustering algorithms while ensuring low resource consumption and result correctness. The framework includes a dynamic partitioning strategy for continuous load-balancing and a cluster-merging algorithm based on convex hulls [10], which guarantees the result correctness. Extensive experiments on real dataset prove the effectiveness of our proposed framework and its advantage over existing solutions.

关键词： Real-time cluster analysis Distributed stream processing spatial-temporal data mining Top-k query Parallel computing

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