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19th ACM SIGKDD International Conference on Knowledge Discovery and data mining (KDD)

作者： Wang, Dawei Ding, Wei Yu, Kui Wu, Xindong Chen, Ping Small, David L. Islam, Shafiqul Univ Massachussets Dept Comp Sci Boston MA 02125 USA Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Anhui Peoples R China Univ Vermont Dept Comp Sci Burlington VT USA Univ Houston Downtown Comp & Math Sci Dept Houston TX USA Tufts Univ Dept Civil & Environm Engn Medford MA 02155 USA

ISBN: (纸本)9781450321747

The development of disastrous flood forecasting techniques able to provide warnings at a long lead-time (5-15 days) is of great importance to society. Extreme Flood is usually a consequence of a sequence of precipitation events occurring over from several days to several weeks. Though precise short-term forecasting the magnitude and extent of individual precipitation event is still beyond our reach, long-term forecasting of precipitation clusters can be attempted by identifying persistent atmospheric regimes that are conducive for the precipitation clusters. However, such forecasting will suffer from overwhelming number of relevant features and high imbalance of sample sets. In this paper, we propose an integrated data mining framework for identifying the precursors to precipitation event clusters and use this information to predict extended periods of extreme precipitation and subsequent floods. We synthesize a representative feature set that describes the atmosphere motion, and apply a streaming feature selection algorithm to online identify the precipitation precursors from the enormous feature space. A hierarchical re-sampling approach is embedded in the framework to deal with the imbalance problem. An extensive empirical study is conducted on historical precipitation and associated flood data collected in the State of Iowa. Utilizing our framework a few physically meaningful precipitation cluster precursor sets are identified from millions of features. More than 90% of extreme precipitation events are captured by the proposed prediction model using precipitation cluster precursors with a lead time of more than 5 days.

关键词： Flood Forecasting Online Streaming Feature Selection spatial-temporal data mining

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A Novel Approach for Company Real Workplace Identification via E-commercial data 23

A Novel Approach for Company Real Workplace Identification v...

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31st ACM SIGspatial International Conference on Advances in Geographic Information Systems (ACM SIGspatial GIS)

作者： Ren, Huimin Ruan, Sijie Li, Yanhua Yuan, Ye Bao, Jie He, Tianfu He, Huajun Meng, Chuishi Zheng, Yu Worcester Polytech Inst Worcester MA 01609 USA JD Technol JD iCity Beijing Peoples R China JD Intelligent Cities Res Beijing Peoples R China Beijing Inst Technol Beijing Peoples R China Southwest Jiaotong Univ Chengdu Peoples R China

ISBN: (纸本)9798400701689

Urban growth benefits significantly from local business development. However, factors like traffic and labor shortages sometimes cause companies to operate away from their registered addresses, resulting in governance challenges. This paper introduces "LocRecognizer," a data mining method that leverages e-commerce data to pinpoint companies' real-world operational locations. Based on the principle that areas with a high concentration of company-related users likely indicate actual workplaces, LocRecognizer combines hierarchical clustering with a deep learning model for accurate detection. When tested on datasets from Beijing and Nantong, it outperformed six baselines. A practical implementation of this system has been operational in Nantong since September 2021, attesting to its effectiveness.

关键词： Location detection Geographic information system spatial-temporal data mining

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Incremental Clustering Approach for Evolving Trajectory data Stream 6

Incremental Clustering Approach for Evolving Trajectory Data...

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6th International Electrical Engineering Congress (iEECON)

作者： Shein, Thi Thi Puntheeranurak, Sutheera King Mongkuts Inst Technol Ladkrabang Dept Comp Engn Fac Engn Bangkok Thailand

ISBN: (纸本)9781538623176

Trajectory data stream contain an enormous amount of data about spatial and temporal information of moving objects. Clustering the trajectory may bring benefits to several applications such as traffic monitoring system, behavior analysis of animal movement pattern, weather forecasting. In many real applications, trajectory data keep coming into the database or server for immediate analysis. Most existing approaches analyze the whole object trajectory from the static database rather than the current movement dynamic data. These methods cannot get a concise result because the objects always move then their position has changed over time. In this paper, we address the problem of monitoring the evolution of moving objects over time and propose incremental Sub-Trajectory Clustering based on Micro-group (iSTCM) framework to reduce computational time complexity. As an experiment, the performance of our proposed algorithm will conduct on real taxicab datasets and compare the efficiency and cluster quality as effectiveness with another state of the art methods.

关键词： spatial-temporal data mining evolving data stream sub-trajectory clustering

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Cross-View Location Alignment Enhanced spatial-Topological Aware Dual Transformer for Travel Time Estimation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第12期25卷 20508-20522页

作者： Zhang, Hanyuan Zhang, Xinyu Jiang, Qize Li, Liang Zheng, Baihua Sun, Weiwei Fudan Univ Sch Comp Sci Shanghai 201203 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 188065 Singapore

Accurately estimating route travel time is crucial for intelligent transportation systems. Urban road networks and routes can be viewed from spatial and topological perspectives while existing works typically focus on one view and disregard important information from the other perspective. In this paper, we propose TTEFORMER, a novel travel time estimation model. It incorporates an alignment-enhanced spatial-topological aware dual transformer model to adaptively incorporate intra- and inter-view features in the route, guided by cross-view location alignment matrices with clear correspondences between locations in two views. Additionally, we propose a sparsity-aware dual-view traffic feature extraction module to effectively capture temporal traffic state changes. Compared to baseline models, TTEFORMER demonstrates improved performance on the MAPE and MAE metrics for Chengdu and Shanghai datasets, achieving improvements of 8.32%, 7.03%, 8.06% and 9.51% respectively, validating the effectiveness of TTEFORMER in travel time estimation.

关键词： Travel time estimation transformer spatial-temporal data mining multi-view learning

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Fine-Grained Trajectory-Based Travel Time Estimation for Multi-City Scenarios Based on Deep Meta-Learning

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 15716-15728页

作者： Wang, Chenxing Zhao, Fang Zhang, Haichao Luo, Haiyong Qin, Yanjun Fang, Yuchen Beijing Univ Posts & Telecommun Natl Pilot Software Engn Sch Sch Comp Sci Beijing 100876 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing 100080 Peoples R China

Travel Time Estimation (TTE) is indispensable in intelligent transportation system (ITS). It is significant to achieve the fine-grained Trajectory-based Travel Time Estimation (TTTE) for multi-city scenarios, namely to accurately estimate travel time of the given trajectory for multiple city scenarios. However, it faces great challenges due to complex factors including dynamic temporal dependencies and fine-grained spatial dependencies. To tackle these challenges, we propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time by leveraging well-designed deep neural network model called DED, which consists of data preprocessing module and Encoder-Decoder network module. By introducing meta learning techniques, the generalization ability of MetaTTE is enhanced using small amount of examples, which opens up new opportunities to increase the potential of achieving consistent performance on TTTE when traffic conditions and road networks change over time in the future. The DED model adopts an encoder-decoder network to capture fine-grained spatial and temporal representations. Extensive experiments on two real-world datasets are conducted to confirm that our MetaTTE outperforms nine state-of-art baselines, and improve 29.35% and 25.93% accuracy than the best baseline on Chengdu and Porto datasets, respectively.

关键词： Estimation Trajectory Task analysis Urban areas Roads data models Global Positioning System spatial-temporal data mining travel time estimation meta learning deep learning

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Diff-RNTraj: A Structure-Aware Diffusion Model for Road Network-Constrained Trajectory Generation

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2024年第12期36卷 7940-7953页

作者： Wei, Tonglong Lin, Youfang Guo, Shengnan Lin, Yan Huang, Yiheng Xiang, Chenyang Bai, Yuqing Wan, Huaiyu Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

Trajectory data is essential for various applications. However, publicly available trajectory datasets remain limited in scale due to privacy concerns, which hinders the development of trajectory mining and applications. Although some trajectory generation methods have been proposed to expand dataset scale, they generate trajectories in the geographical coordinate system, posing two limitations for practical applications: 1) failing to ensure that the generated trajectories are road-constrained. 2) lacking road-related information. In this paper, we propose a new problem, road network-constrained trajectory (RNTraj) generation, which can directly generate trajectories on the road network with road-related information. Specifically, RNTraj is a hybrid type of data, in which each point is represented by a discrete road segment and a continuous moving rate. To generate RNTraj, we design a diffusion model called Diff-RNTraj, which can effectively handle the hybrid RNTraj using a continuous diffusion framework by incorporating a pre-training strategy to embed hybrid RNTraj into continuous representations. During the sampling stage, a RNTraj decoder is designed to map the continuous representation generated by the diffusion model back to the hybrid RNTraj format. Furthermore, Diff-RNTraj introduces a novel loss function to enhance trajectory's spatial validity. Extensive experiments conducted on two datasets demonstrate the effectiveness of Diff-RNTraj.

关键词： spatial-temporal data mining diffusion model trajectory generation road network spatial-temporal data mining diffusion model trajectory generation road network

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Forecasting traffic flow with spatial-temporal convolutional graph attention networks

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NEURAL COMPUTING & APPLICATIONS 2022年第18期34卷 15457-15479页

作者： Zhang, Xiyue Xu, Yong Shao, Yizhen South China Univ Technol Guangzhou Peoples R China

Traffic flow prediction is crucial for intelligent transportation system, such as traffic management, congestion alleviation and public risk assessment. Recently, attention mechanism and deep neural networks are utilized to capture traffic dependencies. However, two challenges have yet to be well addressed: (i) previous works overlook the global dependencies across different regions;(ii) how to integrate spatial and temporal information aggregation with latent channel-aware semantics. To tackle these issues, we propose a deep spatial-temporal convolutional graph attention network for citywide traffic flow prediction. We first apply the multi-resolution transformer network to capture traffic dependencies among different regions with the encoding of multi-level periodicity. spatial dependencies are captured by the attentive graph neural networks followed by convolutional networks from local view to global view. We further propose to inject spatial contextual signals into our framework with the designed channel-aware recalibration residual network, which effectively endows model with the capability of mapping spatial-temporal data patterns into different representation subspaces of latent semantics. The extensive experiments on four real-world datasets demonstrate at least 5% performance gain of our framework by comparing with 19 state-of-the-art traffic prediction methods.

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

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Micro-Macro spatial-temporal Graph-Based Encoder-Decoder for Map-Constrained Trajectory Recovery

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2024年第11期36卷 6574-6587页

作者： Wei, Tonglong Lin, Youfang Lin, Yan Guo, Shengnan Zhang, Lan 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

Recovering intermediate missing GPS points in a sparse trajectory, while adhering to the constraints of the road network, could offer deep insights into users' moving behaviors in intelligent transportation systems. Although recent studies have demonstrated the advantages of achieving map-constrained trajectory recovery via an end-to-end manner, they still face two significant challenges. First, existing methods are mostly sequence-based models. It is extremely hard for them to comprehensively capture the micro-semantics of individual trajectory, including the information of each GPS point and the movement between two GPS points. Second, existing approaches ignore the impact of the macro-semantics, i.e., the road conditions and the people's shared travel preferences reflected by a group of trajectories. To address the above challenges, we propose a Micro-Macro spatial-temporal Graph-based Encoder-Decoder (MM-STGED). Specifically, we model each trajectory as a graph to efficiently describe the micro-semantics of trajectory and design a novel message-passing mechanism to learn trajectory representations. Additionally, we extract the macro-semantics of trajectories and further incorporate them into a well-designed graph-based decoder to guide trajectory recovery. Extensive experiments conducted on sparse trajectories with three different sampling intervals that are respectively constructed from two real-world trajectory datasets demonstrate the superiority of our proposed model.

关键词： Trajectory Global Positioning System Roads Hidden Markov models Task analysis data mining Pipelines Graph neural network macro-semantics map-constrained trajectory recovery micro-semantics spatial-temporal data mining

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History-enhanced and Uncertainty-aware Trajectory Recovery via Attentive Neural Network

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM data 2024年第3期18卷 1-22页

作者： Xia, Tong Li, Yong Qi, Yunhan Feng, Jie Xu, Fengli Sun, Funing Guo, Diansheng Jin, Depeng Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn 30 Shuangqing Rd Beijing Peoples R China Tencent Inc Bldg 910 Xibeiwang East Rd Beijing Peoples R China

A considerable amount of mobility data has been accumulated due to the proliferation of location-based services. Nevertheless, compared with mobility data from transportation systems like the GPS module in taxis, this kind of data is commonly sparse in terms of individual trajectories in the sense that users do not access mobile services and contribute their data all the time. Consequently, the sparsity inevitably weakens the practical value of the data even if it has a high user penetration rate. To solve this problem, we propose a novel attentional neural network-based model, named AttnMove, to densify individual trajectories by recovering unobserved locations at a fine-grained spatial-temporal resolution. To tackle the challenges posed by sparsity, we design various intra- and inter- trajectory attention mechanisms to better model the mobility regularity of users and fully exploit the periodical pattern from long-term history. In addition, to guarantee the robustness of the generated trajectories to avoid harming downstream applications, we also exploit the Bayesian approximate neural network to estimate the uncertainty of each imputation. As a result, locations generated by the model with high uncertainty will be excluded. We evaluate our model on two real-world datasets, and extensive results demonstrate the performance gain compared with the state-of-the-art methods. In-depth analyses of each design of our model have been conducted to understand their contribution. We also show that, by providing high-quality mobility data, our model can benefit a variety of mobility-oriented downstream applications.

关键词： Mobility spatial-temporal data mining self-attention data augmentation

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MVSTT: A Multiview spatial-temporal Transformer Network for Traffic-Flow Forecasting

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第3期54卷 1582-1595页

作者： Pu, Bin Liu, Jiansong Kang, Yan Chen, Jianguo Yu, Philip S. Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Yunnan Univ Natl Pilot Sch Software Kunming 650106 Peoples R China Sun Yat Sen Univ Sch Software Engn Zhuhai 519082 Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA Tsinghua Univ Inst Data Sci Beijing 100084 Peoples R China

Accurate traffic-flow prediction remains a critical challenge due to complicated spatial dependencies, temporal factors, and unpredictable events. Most existing approaches focus on single-or dual-view learning and thus face limitations in systematically learning complex spatial-temporal features. In this work, we propose a novel multiview spatial-temporal transformer (MVSTT) network that can effectively learn complex spatial-temporal domain correlations and potential patterns from multiple views. First, we examine a temporal view and design a short-range gated convolution component from a short-term subview, and a long-range gated convolution component from a long-term subview. These two components effectively aggregate knowledge of the temporal domain at multiple granularities and mine patterns of node evolution across time steps. Meanwhile, in the spatial view, we design a dual-graph spatial learning module that captures fixed and dynamic spatial dependencies of nodes, as well as the evolution patterns of edges, from the static and dynamic graph subviews, respectively. In addition, we further design a spatial-temporal transformer to mine different levels of spatial-temporal features through multiview knowledge fusion. Extensive experiments on four real-world traffic datasets show that our method consistently outperforms the state-of-the-art baseline.

关键词： Deep neural network multiview learning spatial-temporal data mining traffic-flow forecasting

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