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检索条件"主题词=spatial-temporal data mining"

共 57 条记录，以下是31-40 订阅

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MiST: A Multiview and Multimodal spatial-temporal Learning Framework for Citywide Abnormal Event Forecasting 19

MiST: A Multiview and Multimodal Spatial-Temporal Learning F...

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World Wide Web Conference (WWW)

作者： Huang, Chao Zhang, Chuxu Zhao, Jiashu Wu, Xian Yin, Dawei Chawla, Nitesh V. Univ Notre Dame Notre Dame IN 46556 USA JD Com Beijing Peoples R China

ISBN: (纸本)9781450366748

Citywide abnormal events, such as crimes and accidents, may result in loss of lives or properties if not handled efficiently. It is important for a wide spectrum of applications, ranging from public order maintaining, disaster control and people's activity modeling, if abnormal events can be automatically predicted before they occur. However, forecasting different categories of citywide abnormal events is very challenging as it is affected by many complex factors from different views: (i) dynamic intra-region temporal correlation;(ii) complex inter-region spatial correlations;(iii) latent cross-categorical correlations. In this paper, we develop a Multi-View and Multi-Modal patial-temporal learning (MiST) framework to address the above challenges by promoting the collaboration of different views (spatial, temporal and semantic) and map the multi-modal units into the same latent space. Specifically, MiST can preserve the underlying structural information of multi-view abnormal event data and automatically learn the importance of view-specific representations, with the integration of a multi-modal pattern fusion module and a hierarchical recurrent framework. Extensive experiments on three real-world datasets, i.e., crime data and urban anomaly data, demonstrate the superior performance of our MiST method over the state-of-the-art baselines across various settings.

关键词： Abnormal Event Forecasting Deep Neural Networks spatial-temporal data mining

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ST-iFGSM: Enhancing Robustness of Human Mobility Signature Identification Model via spatial-temporal Iterative FGSM 23

ST-iFGSM: Enhancing Robustness of Human Mobility Signature I...

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29th ACM SIGKDD Conference on Knowledge Discovery and data mining (KDD)

作者： Hu, Mingzhi Zhang, Xin Li, Yanhua Zhou, Xun Luo, Jun Worcester Polytech Inst Worcester MA 01609 USA Univ Iowa Iowa City IA 52242 USA Lenovo Grp Ltd Beijing Peoples R China

ISBN: (纸本)9798400701030

The Human Mobility Signature Identification (HuMID) problem aims at determining whether the incoming trajectories were generated by a claimed agent from the historical movement trajectories of a set of individual human agents such as pedestrians and taxi drivers. The HuMID problem is significant, and its solutions have a wide range of real-world applications, such as criminal identification for police departments, risk assessment for auto insurance providers, driver verification in ride-sharing services, and so on. Though Deep neural networks (DNN) based HuMID models on spatial-temporal mobility fingerprint similarity demonstrate remarkable performance in effectively identifying human agents' mobility signatures, it is vulnerable to adversarial attacks as other DNN-based models. Therefore, in this paper, we propose a spatial-temporal iterative Fast Gradient Sign Method with L-0 regularization - ST-iFGSM - to detect the vulnerability and enhance the robustness of HuMID models. Extensive experiments with real-world taxi trajectory data demonstrate the efficiency and effectiveness of our ST-iFGSM algorithm. We tested our method on both the ST-SiameseNet and an LSTM-based HuMID classification model. It shows that ST-iFGSM can generate successful attacks to fool the HuMID models with only a few steps of attack in a small portion of the trajectories. The generated attacks can be used as augmented data to update and improve the HuMID model accuracy significantly from 47.36% to 76.18% on testing samples after the attack (86.25% on the original testing samples).

关键词： spatial-temporal data mining Driver identification Adversarial attack Adversarial training

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FairST: Equitable spatial and temporal Demand Prediction for New Mobility Systems 19

FairST: Equitable Spatial and Temporal Demand Prediction for...

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27th ACM SIGspatial International Conference on Advances in Geographic Information Systems (ACM SIGspatial GIS)

作者： Yan, An Howe, Bill Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781450369091

We present a fairness-aware model for predicting demand for new mobility systems. Our approach, called FairST, consists of 1D, 2D and 3D convolutions to learn the spatial-temporal dynamics of a mobility system, and fairness regularizers that guide the model to make equitable predictions. We propose two fairness metrics, region-based fairness gap (RFG) and individual-based fairness gap (IFG), that measure equity gaps between social groups for new mobility systems. Experimental results on two real-world datasets demonstrate the effectiveness of the proposed model: FairST not only reduces the fairness gap by more than 80%, but achieves better accuracy than state-of-the-art but fairness-oblivious methods including LSTMs, ConvLSTMs, and 3D CNN.

关键词： fairness in machine learning convolutional neural networks equity new mobility spatial-temporal data mining

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TrajGAIL: Trajectory Generative Adversarial Imitation Learning for Long-term Decision Analysis 20

TrajGAIL: Trajectory Generative Adversarial Imitation Learni...

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20th IEEE International Conference on data mining (ICDM)

作者： Zhang, Xin Li, Yanhua Zhou, Xun Zhang, Ziming Luo, Jun Worcester Polytech Inst Worcester MA 01609 USA Univ Iowa Iowa City IA 52242 USA Lenovo Grp Ltd Hong Kong Peoples R China

ISBN: (纸本)9781728183169

Mobile sensing and information technology have enabled us to collect a large amount of mobility data from human decision-makers, for example, GPS trajectories from taxis, Uber cars, and passenger trip data of taking buses and trains. Understanding and learning human decision-making strategies from such data can potentially promote individual's well-being and improve the transportation service quality. Existing works on human strategy learning, such as inverse reinforcement learning, all model the decision-making process as a Markov decision process, thus assuming the Markov property. In this work, we show that such Markov property does not hold in real-world human decision-making processes. To tackle this challenge, we develop a Trajectory Generative Adversarial Imitation Learning (TrajGAIL) framework. It captures the long-term decision dependency by modeling the human decision processes as variable length Markov decision processes (VLMDPs), and designs a deep-neural-network-based framework to inversely learn the decision-making strategy from the human agent's historical dataset. We validate our framework using two real world human-generated spatial-temporal datasets including taxi driver passenger-seeking decision data and public transit trip data. Results demonstrate significant accuracy improvement in learning human decision-making strategies, when comparing to baselines with Markov property assumptions.

关键词： spatial-temporal data mining human decision analysis inverse reinforcement learning imitation learning

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Hierarchically Structured Transformer Networks for Fine-Grained spatial Event Forecasting 20

Hierarchically Structured Transformer Networks for Fine-Grai...

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29th World Wide Web Conference (WWW)

作者： Wu, Xian Huang, Chao Zhang, Chuxu Chawla, Nitesh, V Univ Notre Dame Notre Dame IN 46556 USA JD Finance Amer Corp Mountain View CA 94043 USA Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN USA

ISBN: (纸本)9781450370233

spatial event forecasting is challenging and crucial for urban sensing scenarios, which is beneficial for a wide spectrum of spatial-temporal mining applications, ranging from traffic management, public safety, to environment policy making. In spite of significant progress has been made to solve spatial-temporal prediction problem, most existing deep learning based methods based on a coarse-grained spatial setting and the success of such methods largely relies on data sufficiency. In many real-world applications, predicting events with a fine-grained spatial resolution do play a critical role to provide high discernibility of spatial-temporal data distributions. However, in such cases, applying existing methods will result in weak performance since they may not well capture the quality spatial-temporal representations when training triple instances are highly imbalanced across locations and time. To tackle this challenge, we develop a hierarchically structured spatial-temporal ransformer network (STtrans) which leverages a main embedding space to capture the inter-dependencies across time and space for alleviating the data imbalance issue. In our STtrans framework, the first-stage transformer module discriminates different types of region and time-wise relations. To make the latent spatial-temporal representations be reflective of the relational structure between categories, we further develop a cross-category fusion transformer network to endow STtrans with the capability to preserve the semantic signals in a fully dynamic manner. Finally, an adversarial training strategy is introduced to yield a robust spatialtemporal learning under data imbalance. Extensive experiments on real-world imbalanced spatial-temporal datasets from NYC and Chicago demonstrate the superiority of our method over various state-of-the-art baselines.

关键词： spatial-temporal data mining Deep neural networks

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A "Semi-Lazy" Approach to Probabilistic Path Prediction in Dynamic Environments 13

A "Semi-Lazy" Approach to Probabilistic Path Prediction in D...

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

作者： Zhou, Jingbo Tung, Anthony K. H. Wu, Wei Ng, Wee Siong Natl Univ Singapore Sch Comp Singapore Singapore ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781450321747

Path prediction is useful in a wide range of applications. Most of the existing solutions, however, are based on eager learning methods where models and patterns are extracted from historical trajectories and then used for future prediction. Since such approaches are committed to a set of statistically significant models or patterns, problems can arise in dynamic environments where the underlying models change quickly or where the regions are not covered with statistically significant models or patterns. We propose a "semi-lazy" approach to path prediction that builds prediction models on the fly using dynamically selected reference trajectories. Such an approach has several advantages. First, the target trajectories to be predicted are known before the models are built, which allows us to construct models that are deemed relevant to the target trajectories. Second, unlike the lazy learning approaches, we use sophisticated learning algorithms to derive accurate prediction models with acceptable delay based on a small number of selected reference trajectories. Finally, our approach can be continuously self-correcting since we can dynamically re-construct new models if the predicted movements do not match the actual ones. Our prediction model can construct a probabilistic path whose probability of occurrence is larger than a threshold and which is furthest ahead in term of time. Users can control the confidence of the path prediction by setting a probability threshold. We conducted a comprehensive experimental study on real-world and synthetic datasets to show the effectiveness and efficiency of our approach.

关键词： Trajectory Analysis spatial-temporal data mining Lazy Learning

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Pre-Training Identification of Graph Winning Tickets in Adaptive spatial-temporal Graph Neural Networks 24

Pre-Training Identification of Graph Winning Tickets in Adap...

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30th ACM SIGKDD Conference on Knowledge Discovery and data mining

作者： Duan, Wenying Fang, Tianxiang Rao, Hong He, Xiaoxi Nanchang Univ Jiangxi Prov Key Lab Intelligent Syst & HumanMach Nanchang Peoples R China Nanchang Univ Nanchang Peoples R China Nanchang Univ Sch Software Nanchang Peoples R China Univ Macau Fac Sci & Technol Macau Peoples R China

ISBN: (纸本)9798400704901

In this paper, we present a novel method to significantly enhance the computational efficiency of Adaptive spatial-temporal Graph Neural Networks (ASTGNNs) by introducing the concept of the Graph Winning Ticket (GWT), derived from the Lottery Ticket Hypothesis (LTH). By adopting a pre-determined star topology as a GWT prior to training, we balance edge reduction with efficient information propagation, reducing computational demands while maintaining high model performance. Both the time and memory computational complexity of generating adaptive spatial-temporal graphs is significantly reduced from O(N-2) to O(N). Our approach streamlines the ASTGNN deployment by eliminating the need for exhaustive training, pruning, and retraining cycles, and demonstrates empirically across various datasets that it is possible to achieve comparable performance to full models with substantially lower computational costs. Specifically, our approach enables training ASTGNNs on the largest scale spatial-temporal dataset using a single A6000 equipped with 48 GB of memory, overcoming the out-of-memory issue encountered during original training and even achieving state-of-the-art performance. Furthermore, we delve into the effectiveness of the GWT from the perspective of spectral graph theory, providing substantial theoretical support. This advancement not only proves the existence of efficient subnetworks within ASTGNNs but also broadens the applicability of the LTH in resource-constrained settings, marking a significant step forward in the field of graph neural networks. Code is available at https://***/r/paper-1430.

关键词： spatial-temporal graph neural network lottery ticket hypothesis spatial-temporal data mining

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PROBABILISTIC FINE-GRAINED URBAN FLOW INFERENCE WITH NORMALIZING FLOWS 47

PROBABILISTIC FINE-GRAINED URBAN FLOW INFERENCE WITH NORMALI...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zhong, Ting Yu, Haoyang Li, Rongfan Xu, Xovee Luo, Xucheng Zhou, Fan Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665405409

Fine-grained urban flow inference (FUFI) aims at enhancing the resolution of traffic flow, which plays an important role in intelligent traffic management. Existing FUFI methods are mainly based on techniques from image super-resolution (SR) models, which cannot fully capture the influence of external factors and face the ill-posed problem in SR tasks. In this paper, we propose UFI-Flow - Urban Flow Inference via normalizing Flow, a novel model for addressing the FUFI problem in a principled manner by using a single probabilistic loss. UFI-Flow therefore directly accounts for the ill-posed nature of the problem and learns spatial correlations between urban flow maps. In addition, an augmented distribution fusion mechanism is further proposed to reinforce the influence of external factors in the joint distribution inference. We conduct comprehensive experiments on real-world datasets to show the superiority of the proposed model compared to the state-of-the-art baseline approaches.

关键词： spatial-temporal data mining urban flow inference normalizing flow mobile sensing

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DiffUFlow: Robust Fine-grained Urban Flow Inference with Denoising Diffusion Model 23

DiffUFlow: Robust Fine-grained Urban Flow Inference with Den...

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32nd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Zheng, Yuhao Zhong, Lian Wang, Senzhang Yang, Yu Gu, Weixi Zhang, Junbo Wang, Jianxin Cent South Univ Changsha Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China China Acad Ind Internet Beijing Peoples R China JD Intelligent Cities Res Beijing Peoples R China

ISBN: (纸本)9798400701245

Inferring the fine-grained urban flows based on the coarse-grained flow observations is practically important to many smart city-related applications. However, the collected urban flows are usually rather unreliable, may contain noise and sometimes are incomplete, thus posing great challenges to existing approaches. In this paper, we present a pioneering study on robust fine-grained urban flow inference with noisy and incomplete urban flow observations, and propose a denoising diffusion model named DiffUFlow to effectively address it with an improved reverse diffusion strategy. Specifically, a spatial-temporal feature extraction network called STFormer and a semantic features extraction network called ELFetcher are proposed. Then, we overlay the extracted spatial-temporal feature map onto the coarse-grained flow map, serving as a conditional guidance for the reverse diffusion process. We further integrate the semantic features extracted by ELFetcher to cross-attention layers, enabling the comprehensive consideration of semantic information for fine-grained flow inference. Extensive experiments on two large real-world datasets validate the effectiveness of our method compared with the state-of-the-art baselines.

关键词： spatial-temporal data mining Urban flow inference Denoising diffusion model

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CAC: Enabling Customer-Centered Passenger -Seeking for Self-Driving Ride Service with Conservative Actor-Critic 23

CAC: Enabling Customer-Centered Passenger -Seeking for Self-...

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23rd IEEE International Conference on data mining (IEEE ICDM)

作者： Busaranuvong, Palawat Zhang, Xin Li, Yanhua Zhou, Xun Luo, Jun Worcester Polytechn Inst Worcester MA USA San Diego State Univ San Diego CA USA Univ Iowa Iowa City IA 52242 USA Logist & Supply Chain MultiTech R&D Ctr Hong Kong Peoples R China

ISBN: (纸本)9798350307887

Rapid advances in perception, planning, and decision-making areas for self-driving vehicles have led to great improvements in their function and capabilities and enabled several prototypes to be driving on the roads and streets, such as Waymo Driver, TuSimple, Nuro, etc. Among various applications of self-driving vehicles, a promising one is the ride service as it has the potential to improve service quality and productivity and to provide service to anyone at any time. Extensive studies have been conducted on self-driving planning and safety, but few works focus on self-driving ride service decision -making and routing. In this work, we take the lead to study self-driving ride service planning and decision -making problem leveraging human-generated spatial -temporal data, and propose the data -driven Conservative Actor-Critic approach CAC based on offline reinforcement learning. Our CAC is able to make conservative decisions in a complicated environment with multiple goal states, and avoid dangerous and overly optimistic behaviors by exploiting human decisions. Extensive experiments with real-world data demonstrate that our CAC-learned policies are able to improve taxi service operation efficiency and quality drastically in terms of shortening passenger waiting time and improving service revenue.

关键词： offline reinforcement learning spatial-temporal data mining actor-critic conservative Q-learning

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