Semantic understanding of spatio-temporal trajectories in the era of generative artificial intelligence: challenges, opportunities and developments

• 背景知识
  • 时空轨迹数据概述
  • 常见时空轨迹数据
  • 时空轨迹数据的表示与定义
• 时空轨迹语义理解学习框架
  • 时空图神经网络
  • 概率深度学习
  • 基于底座模型的学习
• 时空轨迹语义理解核心任务
  • 时空要素表示学习
    • 深度表示学习
    • 解耦表示学习
    • 因果表示学习
  • 基于时空语义理解的轨迹相似学习
    • 时空轨迹聚类
    • 时空轨迹分类
    • 异常行为识别
  • 基于时空语义理解的轨迹预测学习
    • 时空轨迹预测
    • 时空事件预测
    • 时空数据补全
• 新智能时代的时空轨迹语义理解的挑战与机遇
  • 多模态数据处理
  • 模型的透明可释
  • 开放语义建模
  • 模型可用与资源消耗
  • 隐私、伦理与信任问题
• 常用数据集

背景知识

时空轨迹数据概述

• Alam M M, Torgo L, Bifet A. 2022. A survey on spatio-temporal data analytics systems. ACM Computing Surveys 2022.
• Erwig M, Gu¨ ting R H, Schneider M, et al. 1999. Spatio-temporal data types: An approach to modeling and querying moving objects in databases. GeoInformatica 1999.
• Gao Q, Zhang FL, Wang RJ, Zhou F. 2017. Trajectory big data: A review of key technologies in data processing. Ruan Jian Xue Bao/Journal of Software 2017.
• Zhang J, Zheng Y, Qi D, et al. 2016. DNN-based prediction model for spatio-temporal data. Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems 2016.
• Yao D, Zhang C, Huang JH, Chen YX, Bi JP. 2018. Semantic understanding of spatio-temporal data: Technology & application. Ruan Jian Xue Bao/Journal of Software 2018.
• Atluri Gowtham, Anuj Karpatne, and Vipin Kumar. 2018. Spatio-temporal data mining: A survey of problems and methods. ACM Computing Surveys (CSUR) 2018.
• Shekhar S, Jiang Z, Ali R Y, et al. 2015. Spatiotemporal data mining: A computational perspective. ISPRS International Journal of Geo-Information 2015.
• Pelekis N, Theodoulidis B, Kopanakis I, et al. 2004. Literature review of spatio-temporal database models. The Knowledge Engineering Review 2004.

常见时空轨迹数据

• Evans M R, Oliver D, Yang K S, et al. 2019. Enabling spatial big data via CyberGIS: Challenges and opportunities. CyberGIS for geospatial discovery and innovation 2019.

时空轨迹数据的表示与定义

• Cao HL, Tang HN, Wang F, Xu YJ. 2021. Survey on trajectory representation learning techniques. Ruan Jian Xue Bao/Journal of Software 2021.
• Li CN, Feng GW, Yao H, Liu RY, Li YN, Xie K, Miao QG. 2024. Survey on Trajectory Anomaly Detection. Ruan Jian Xue Bao/Journal of Software 2024.

时空轨迹语义理解学习框架

• Chen W, Liang Y, Zhu Y, et al. 2024. Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond. arXiv preprint 2024.
• LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 2015.

时空图神经网络（Spatial-Temporal Graph Neural Networks）

• Jinyu Hong, Ping Kuang, Qiang Gao*, Fan Zhou. 2024. Disentanglement-Guided Spatial-Temporal Graph Neural Network for Metro Flow Forecasting(Student Abstract). The Thirty-Eighth AAAI Conference on Artificial Intelligence 2024.
• Qiang Gao, Fan Zhou, Ting Zhong, Goce Trajcevski, Xin Yang, and Tianrui Li. 2022. Contextual Spatio-Temporal Graph Representation Learning for Reinforced Human Mobility Mining. Information Sciences 2022.
• Feng J, Li Y, Zhang C, et al. 2018. Deepmove: Predicting human mobility with attentional recurrent networks. Proceedings of the 2018 world wide web conference 2018 .
• Gao J, Sharma R, Qian C, et al. 2021. STAN: spatio-temporal attention network for pandemic prediction using real-world evidence. Journal of the American Medical Informatics Association 2021.
• Bai S, Kolter J Z, Koltun V. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv: Learning 2018.
• Su Z, Liu T, Hao X, et al. 2023. Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters. The Journal of Supercomputing 2023.
• Zhang K, Feng X, Wu L, et al. 2022. Trajectory prediction for autonomous driving using spatial-temporal graph attention transformer. IEEE Transactions on Intelligent Transportation Systems, 2022.

概率深度学习

• Kingma D P, Welling M. 2013. Auto-encoding variational bayes. arXiv preprint 2013.
• Ramchandran S, Tikhonov G, Lönnroth O, et al. 2024. Learning conditional variational autoencoders with missing covariates. Pattern Recognition 2024.
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• Kim H, Mnih A. 2018. Disentangling by Factorising. International Conference on Machine Learning 2018.
• Duan Y, Wang L, Zhang Q, et al. 2022. Factorvae: A probabilistic dynamic factor model based on variational autoencoder for predicting cross-sectional stock returns. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
• Sohl-Dickstein J, Weiss E, Maheswaranathan N, et al. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. International conference on machine learning 2015.
• Ho J, Jain A, Abbeel P. 2021. Denoising diffusion probabilistic models. Advances in neural information processing systems 2020.
• Song Y, Sohl-Dickstein J, Kingma D P, et al. 2020. Score-based generative modeling through stochastic differential equations. International Conference on Learning Representations 2021.
• Bao Y, Zhou H, Huang S, et al. 2019. Generating Sentences from Disentangled Syntactic and Semantic Spaces. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.
• Lin L, Li Z, Li R, et al. 2023. Diffusion models for time-series applications: a survey. Frontiers of Information Technology & Electronic Engineering 2023.
• Qin Y, Wu H, Ju W, et al. 2023. A diffusion model for poi recommendation. ACM Transactions on Information Systems 2023.
• Lim H, Kim M, Park S, et al. 2023. Regular time-series generation using sgm. arXiv preprint 2023.
• Li Y, Lu X, Wang Y, et al. 2022. Generative time series forecasting with diffusion, denoise, and disentanglement. Advances in Neural Information Processing Systems 2022.
• Zhu Y, Yu J J, Zhao X, et al. 2024. Controltraj: Controllable trajectory generation with topology-constrained diffusion model. arXiv preprint 2024.
• Zhu Y, Ye Y, Zhang S, et al. 2024. Difftraj: Generating gps trajectory with diffusion probabilistic model. Advances in Neural Information Processing Systems 2024.
• Wei T, Lin Y, Guo S, et al. 2024. Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation. arXiv preprint 2024.
• Larsen A B L, Sønderby S K, Larochelle H, et al. 2016. Autoencoding beyond pixels using a learned similarity metric. International conference on machine learning 2016.
• Mescheder L, Nowozin S, Geiger A. 2017. Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks. International conference on machine learning. PMLR, 2017.
• Makhzani A, Shlens J, Jaitly N, et al. 2015. Adversarial autoencoders. arXiv preprint arXiv:1511.05644, 2015.

基于底座模型的学习（Foundation Model-based Learning）

• Brown T, Mann B, Ryder N, et al. 2020. Language Models are Few-Shot Learners. arXiv: Computation and Language 2020.
• Zhou T, Niu P, Sun L, et al. 2023. One fits all: Power general time series analysis by pretrained lm. Advances in neural information processing systems 2023.
• Wang P, Wei X, Hu F, et al. 2024. TransGPT: Multi-modal Generative Pre-trained Transformer for Transportation. arXiv preprint 2024.
• Zhang S, Fu D, Liang W, et al. 2024. TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models. Transport Policy 2024.
• Liu R, Li C, Tang H, et al. 2024. ST-LLM: Large Language Models Are Effective Temporal Learners. arXiv preprint 2024.
• Xue H, Voutharoja B P, Salim F D. 2022. Leveraging language foundation models for human mobility forecasting. Proceedings of the 30th International Conference on Advances in Geographic Information Systems 2022.
• Yang C H H, Tsai Y Y, Chen P Y. 2021. Voice2series: Reprogramming acoustic models for time series classification. International conference on machine learning. PMLR 2021.

时空轨迹语义理解核心任务

时空要素表示学习

• Bengio Y, Courville A, Vincent P. 2013. Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 2013.

深度表示学习（Deep Representation Learning）

• Quan R, Zhu L, Wu Y, et al. 2021. Holistic LSTM for Pedestrian Trajectory Prediction. IEEE Transactions on Image Processing 2021.
• Lin L, Li W, Bi H, et al. 2022. Vehicle Trajectory Prediction Using LSTMs With Spatial–Temporal Attention Mechanisms. IEEE Intelligent Transportation Systems Magazine 2022.
• Karimzadeh M, Aebi R, de Souza A M, et al. 2021. Reinforcement Learning-designed LSTM for Trajectory and Traffic Flow Prediction. 2021 IEEE Wireless Communications and Networking Conference (WCNC) 2021.
• Guo H, Rui L, Gao Z. 2022. V2V Task Offloading Algorithm with LSTM-based Spatiotemporal Trajectory Prediction Model in SVCNs. IEEE Transactions on Vehicular Technology 2022.
• Zhang C, Ni Z, Berger C. 2023. Spatial-Temporal-Spectral LSTM: A Transferable Model for Pedestrian Trajectory Prediction. IEEE Transactions on Intelligent Vehicles 2023.
• Zhou S, Li J, Wang H, et al. 2023. GRLSTM: trajectory similarity computation with graph-based residual LSTM. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
• Yang J, Chen Y, Du S, et al. 2024. IA-LSTM: interaction-aware LSTM for pedestrian trajectory prediction. IEEE transactions on cybernetics 2024.
• Yang C, Pei Z. 2023. Long-Short Term Spatio-Temporal Aggregation for Trajectory Prediction. IEEE Transactions on Intelligent Transportation Systems 2023.
• Ou J, Jin H, Wang X, et al. 2023. STA-TCN: Spatial-temporal Attention over Temporal Convolutional Network for Next Point-of-interest Recommendation. ACM Transactions on Knowledge Discovery from Data 2023.
• Katariya V, Baharani M, Morris N, et al. 2022. Deeptrack: Lightweight deep learning for vehicle trajectory prediction in highways. IEEE Transactions on Intelligent Transportation Systems 2022.
• Sadid H, Antoniou C. 2024. Dynamic Spatio-temporal Graph Neural Network for Surrounding-aware Trajectory Prediction of Autonomous Vehicles. IEEE Transactions on Intelligent Vehicles 2024.
• Lv K, Yuan L. 2023. SKGACN:Social Knowledge-guided Graph Attention Convolutional Network for Human Trajectory Prediction. IEEE Transactions on Instrumentation and Measurement 2023.
• Yuan H, Zhang J, Zhang L, et al. 2023. Vehicle Trajectory Prediction Based on Posterior Distributions Fitting and TCN-Transformer. IEEE Transactions on Transportation Electrification 2023.
• Jiang R, Xu H, Gong G, et al. 2022. Spatial-Temporal Attentive LSTM for Vehicle-Trajectory Prediction. ISPRS International Journal of Geo-Information 2022.
• Hasan F, Huang H. 2023. MALS-Net: A Multi-Head Attention-Based LSTM Sequence-to-Sequence Network for Socio-Temporal Interaction Modelling and Trajectory Prediction. Sensors 2023.
• Liang Y, Zhao Z.. 2022. NetTraj: A Network-Based Vehicle Trajectory Prediction Model With Directional Representation and Spatiotemporal Attention Mechanisms. IEEE Transactions on Intelligent Transportation Systems 2022.
• Zhang K, Zhao L, Dong C, et al. 2023. AI-TP: Attention-Based Interaction-Aware Trajectory Prediction for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2023.
• Schmidt J, Jordan J, Gritschneder F, et al. 2022. Crat-pred: Vehicle trajectory prediction with crystal graph convolutional neural networks and multi-head self-attention. 2022 International Conference on Robotics and Automation (ICRA) IEEE 2022.
• Scarselli F, Gori M, Tsoi A C, et al. 2009. The Graph Neural Network Model. IEEE Transactions on Neural Networks 2009.
• Zhou H, Ren D, Xia H, et al. 2021. AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction. Neurocomputing 2021.
• Zhou F, Chen S, Wu J, et al. 2021. Trajectory-User Linking via Graph Neural Network. ICC 2021 - IEEE International Conference on Communications 2021.
• Han P, Wang J, Yao D, et al. 2021. A Graph-based Approach for Trajectory Similarity Computation in Spatial Networks. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining 2021.
• Zhu W, Liu Y, Wang P, et al. 2023. Tri-HGNN: Learning triple policies fused hierarchical graph neural networks for pedestrian trajectory prediction. Pattern Recognition 2023.
• Li R, Qin Y, Wang J, et al. 2023. AMGB: Trajectory prediction using attention-based mechanism GCN-BiLSTM in IOV. Pattern Recognition Letters 2023.
• Zhou F, Wang P, Xu X, et al. 2021. Contrastive trajectory learning for tour recommendation. ACM Transactions on Intelligent Systems and Technology (TIST) 2021.
• Mao Z, Li Z, Li D, et al. 2022. Jointly Contrastive Representation Learning on Road Network and Trajectory. Proceedings of the 31st ACM International Conference on Information & Knowledge Management 2022.
• Deng L, Zhao Y, Fu Z, et al. 2022. Efficient trajectory similarity computation with contrastive learning. Proceedings of the 31st ACM International Conference on Information & Knowledge Management 2022.
• Chen Z, Zhang D, Feng S, et al. 2024. KGTS: Contrastive Trajectory Similarity Learning over Prompt Knowledge Graph Embedding. Proceedings of the AAAI Conference on Artificial Intelligence 2024.

解耦表示学习（Disentangled Representation Learning）

• Bengio Y, Lamblin P, Popovici D, et al. 2007. Greedy Layer-Wise Training of Deep Networks. Advances in Neural Information Processing Systems 19 2007.
• Higgins I, Amos D, Pfau D, et al. 2018. Towards a definition of disentangled representations. arXiv preprint 2018.
• Goodfellow I, Pouget-Abadie J, Mirza M, et al. 2020. Generative adversarial networks. Communications of the ACM 2020.
• Shwartz-Ziv R, Tishby N. 2017. Opening the black box of deep neural networks via information. arXiv preprint 2017.
• Zhang H, Wu Y, Tan H, et al. 2022. Understanding and Modeling Urban Mobility Dynamics via Disentangled Representation Learning. IEEE Transactions on Intelligent Transportation Systems 2022.
• Bae I, Jeon H G. 2022. Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
• Fang Y, Qin Y, Luo H, et al. 2023. STWave+: A Multi-Scale Efficient Spectral Graph Attention Network With Long-Term Trends for Disentangled Traffic Flow Forecasting. IEEE Transactions on Knowledge and Data Engineering 2023.
• Du Y, Guo X, Cao H, et al. 2022. Disentangled spatiotemporal graph generative models. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
• Fang Y, Qin Y, Luo H, et al. 2021. Spatio-temporal meets wavelet: Disentangled traffic flow forecasting via efficient spectral graph attention network. arXiv preprint 2021.
• Wang Z, Zhu Y, Liu H, et al. 2022. Learning graph-based disentangled representations for next POI recommendation. Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval 2022.
• Qin Y, Wang Y, Sun F, et al. 2023. DisenPOI: Disentangling sequential and geographical influence for point-of-interest recommendation. Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining 2023.
• Gao Q, Hong J, Xu X, et al. 2023. Predicting human mobility via self-supervised disentanglement learning. IEEE Transactions on Knowledge and Data Engineering 2023.
• Qin Y, Gao C, Wang Y, et al. 2022. Disentangling Geographical Effect for Point-of-Interest Recommendation. IEEE Transactions on Knowledge and Data Engineering 2022.
• Tao H, Zeng J, Wang Z, et al. 2023. Next POI Recommendation Based on Spatial and Temporal Disentanglement Representation. 2023 IEEE International Conference on Web Services (ICWS). IEEE 2023.

因果表示学习（Disentangled Representation Learning）

• Swanson N R, Granger C W J. 1997. Impulse Response Functions Based on a Causal Approach to Residual Orthogonalization in Vector Autoregressions. Journal of the American Statistical Association 1997.
• Hyvärinen A, Zhang K, Shimizu S, et al. 2010. Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. Journal of Machine Learning Research 2010.
• Yao W, Sun Y, Ho A, et al. 2021. Learning temporally causal latent processes from general temporal data. arXiv preprint 2021.
• Song X, Yao W, Fan Y, et al. 2024. Temporally Disentangled Representation Learning under Unknown Nonstationarity. Advances in Neural Information Processing Systems 2024.
• Huang B, Zhang K, Zhang J, et al. 2019. Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes. arXiv: Learning 2019.
• Zhang K, Gong M, Stojanov P, et al. 2020. Domain Adaptation as a Problem of Inference on Graphical Models. Neural Information Processing Systems 2020.

基于时空语义理解的轨迹相似学习

时空轨迹聚类

• Krishna K, Murty M N. 1999. Genetic K-means algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 1999.
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• Ankerst M, Breunig M M, Kriegel H P, et al. 1999. OPTICS: Ordering points to identify the clustering structure. ACM Sigmod record 1999.
• Zhang T, Ramakrishnan R, Livny M. 1996. BIRCH: an efficient data clustering method for very large databases. ACM sigmod record 1996.
• Wang W, Yang J, Muntz R. 1997. STING: A statistical information grid approach to spatial data mining. Vldb. 1997.
• Birant D, Kut A. 2007. ST-DBSCAN: An algorithm for clustering spatial–temporal data. Data & Knowledge Engineering 2007.
• Malhan A, Gunturi V M V, Naik V. 2017. ST-OPTICS: A spatial-temporal clustering algorithm with time recommendations for taxi services. 2017.
• Li X, Zhao K, Cong G, et al. 2018. Deep Representation Learning for Trajectory Similarity Computation. 2018 IEEE 34th International Conference on Data Engineering (ICDE) 2018.
• Yue M, Li Y, Yang H, et al. 2020. DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis. Cornell University - arXiv 2020.
• Fang Z, Du Y, Chen L, et al. 2021. E2DTC: An End to End Deep Trajectory Clustering Framework via Self-Training. 2021 IEEE 37th International Conference on Data Engineering (ICDE) 2021.
• Si J, Yang J, Xiang Y, et al. 2024. ConDTC: Contrastive Deep Trajectory Clustering for Fine-grained Mobility Pattern Mining. IEEE Transactions on Big Data 2024.
• Jiang Q, Yu L I U, Ziran D, et al. 2023. Behavior pattern mining based on spatiotemporal trajectory multidimensional information fusion. Chinese Journal of Aeronautics 2023.
• Wang W, Xia F, Nie H, et al. 2021. Vehicle Trajectory Clustering Based on Dynamic Representation Learning of Internet of Vehicles. IEEE Transactions on Intelligent Transportation Systems 2021.
• Hoseini F, Rahrovani S, Chehreghani M H. 2021. Vehicle Motion Trajectories Clustering via Embedding Transitive Relations. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.

时空轨迹分类

• Fan Zhou, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, Fengli Zhang. 2018. Trajectory-User Linking via Variational AutoEncoder. IJCAI 2018.
• Oh J, Lim K T, Chung Y S. 2021. TrajNet: An Efficient and Effective Neural Network for Vehicle Trajectory Classification. Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods 2021.
• Bae K, Lee S, Lee W. 2022. Transformer Networks for Trajectory Classification. 2022 IEEE International Conference on Big Data and Smart Computing (BigComp) 2022.
• Liang Y, Ouyang K, Wang Y, et al. 2022. TrajFormer: Efficient trajectory classification with transformers. Proceedings of the 31st ACM International Conference on Information & Knowledge Management 2022.
• Ahmed U, Srivastava G, Djenouri Y, et al. 2021. Knowledge graph based trajectory outlier detection in sustainable smart cities. Sustainable Cities and Society 2022.
• Guo T, Xie L. 2022. Research on Ship Trajectory Classification Based on a Deep Convolutional Neural Network. Journal of Marine Science and Engineering 2022.
• de Freitas N C A, da Silva T L C, de Macêdo J A F, et al. 2021. Using deep learning for trajectory classification in imbalanced dataset. The International FLAIRS Conference Proceedings 2021.
• Landi C, Spinnato F, Guidotti R, et al. 2023. Geolet: An Interpretable Model for Trajectory Classification. Advances in Intelligent Data Analysis XXI,Lecture Notes in Computer Science 2023.
• Ferrero C A, Petry L M, Alvares L O, et al. 2020. MasterMovelets: discovering heterogeneous movelets for multiple aspect trajectory classification. Data Mining and Knowledge Discovery 2020.
• Gao Q, Zhou F, Zhang K, et al. 2017. Identifying Human Mobility via Trajectory Embeddings. IJCAI 2017.
• Hu X, Han Y, Geng Z. 2021. Novel Trajectory Representation Learning Method and Its Application to Trajectory-User Linking. IEEE Transactions on Instrumentation and Measurement 2021.
• Chen W, Li S, Huang C, et al. 2022. Mutual distillation learning network for trajectory-user linking. arXiv preprint 2022.
• Alsaeed M, Agrawal A, Papagelis M. 2023. Trajectory-User Linking using Higher-order Mobility Flow Representations. 2023 24th IEEE International Conference on Mobile Data Management (MDM). IEEE 2023.
• Deng L, Sun H, Zhao Y, et al. 2023. S2tul: A semi-supervised framework for trajectory-user linking. Proceedings of the sixteenth ACM international conference on web search and data mining 2023.
• Chen W, Huang C, Yu Y, et al. 2024. Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks. ACM Transactions on Knowledge Discovery from Data 2024.

异常行为识别

• Qiang Gao, Xiaohan Wang, Chaoran Liu, Goce Trajcevski, Li Huang, Fan Zhou. 2023. Open Anomalous Trajectory Recognition via Probabilistic Metric Learning The 32nd International Joint Conference on Artificial Intelligence (IJCAI) 2023.
• Santhosh K K, Dogra D P, Roy P P, et al. 2022. Vehicular Trajectory Classification and Traffic Anomaly Detection in Videos Using a Hybrid CNN-VAE Architecture. IEEE Transactions on Intelligent Transportation Systems 2022.
• Wiederer J, Bouazizi A, Troina M, et al. 2022. Anomaly detection in multi-agent trajectories for automated driving. Conference on Robot Learning. PMLR 2022.
• Su Y, Yao D, Bi J. 2023. Transfer learning for region-wide trajectory outlier detection. IEEE Access 2023.
• Han X, Cheng R, Ma C, et al. 2022. DeepTEA: Effective and efficient online time-dependent trajectory outlier detection. Proceedings of the VLDB Endowment 2022.
• Li C, Feng G, Jia Y, et al. 2023. RETAD: Vehicle Trajectory Anomaly Detection Based on Reconstruction Error. International Journal of Data Warehousing & Mining 2023.
• Xie L, Guo T, Chang J, et al. 2023. A Novel Model for Ship Trajectory Anomaly Detection Based on Gaussian Mixture Variational Autoencoder. IEEE Transactions on Vehicular Technology 2023.
• Singh S K, Fowdur J S, Gawlikowski J, et al. 2021. Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Trajectories. arXiv preprint 2021.
• Makris A, Kontopoulos I, Psomakelis E, et al. 2021. Semi-supervised trajectory classification using convolutional auto-encoders. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility 2021.
• Djenouri Y, Djenouri D, Lin J C W. 2021. Trajectory outlier detection: New problems and solutions for smart cities. ACM Transactions on Knowledge Discovery from Data (TKDD) 2021.
• Mao J, Liu J, Jin C, et al. 2021. Feature Grouping–based Trajectory Outlier Detection over Distributed Streams. ACM Transactions on Intelligent Systems and Technology 2021.
• Ahmed U, Srivastava G, Djenouri Y, et al. 2022. Deviation Point Curriculum Learning for Trajectory Outlier Detection in Cooperative Intelligent Transport Systems. IEEE Transactions on Intelligent Transportation Systems 2022.
• JIN C, TAO T, LUO X, et al. 2020. S2N2: An Interpretive Semantic Structure Attention Neural Network for Trajectory Classification. IEEE Access 2020.
• SHI Y, WANG D, NI Z, et al. 2022. A Sequential Pattern Mining Based Approach to Adaptively Detect Anomalous Paths in Floating Vehicle Trajectories. IEEE Transactions on Intelligent Transportation Systems 2022.
• GAO L, XU C, WANG F, et al. 2023. Flight data outlier detection by constrained LSTM-autoencoder. Wireless Networks 2023.

基于时空语义理解的轨迹预测学习

时空轨迹预测

• Li Huang, Kai Liu, Chaoran Liu, Qiang Gao*, Xiao Zhou, and Guisong Liu. 2023. HBay: Predicting Human Mobility via Hyperspherical Bayesian Learning. The 16th International Conference on Knowledge Science Engineering and Management (KSEM) 2023.
• Qiang Gao, Hongzhu Fu, Kunpeng Zhang, Goce Trajcevski, Xu Teng, and Fan Zhou. 2023. Inferring Real Mobility in Presence of Fake Check-ins Data. ACM Transactions on Intelligent Systems and Technology 2023.
• Jinyu Hong, Fan Zhou, Qiang Gao*, Ping Kuang, and Kunpeng Zhang. 2023. Mobility Prediction via Sequential Trajectory Disentanglement (Student Abstract). The Thirty-Seventh AAAI Conference on Artificial Intelligence 2023.
• Xovee Xu, Zhiyuan Wang, Qiang Gao, Ting Zhong, Bei Hui, Fan Zhou, and Goce Trajcevski. 2023. Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference. IEEE Transactions on Big Data 2023.
• Qiang Gao, Jinyu Hong, Xovee Xu, Ping Kuang, Fan Zhou, and Goce Trajcevski. 2023. Predicting Human Mobility via Self-supervised Disentanglement Learning. IEEE Transactions on Knowledge and Data Engineering 2023.
• Qiang Gao, Wei Wang, Li Huang, Xin Yang, Tianrui Li, and Hamido Fujita. 2023. Dual-grained Human Mobility Learning for Location-aware Trip Recommendation with Spatial-temporal Graph Knowledge Fusion. Information Fusion 2023.
• Qiang Gao, Fan Zhou, Xin Yang, and Guisong Liu. 2023. When Friendship Meets Sequential Human Check-ins: Inferring Social Circles with Variational Mobility. Neurocomputing 2023.
• Fan Zhou, Rongfan Li, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, and Ting Zhong. 2022. Dynamic Manifold Learning for Land Deformation Forecasting. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI) 2022.
• Fan Zhou, Yurou Dai, Qiang Gao, Pengyu Wang, and Ting Zhong. 2021. Self-Supervised Human Mobility Learning for Next Location Prediction and Trajectory Classification. Knowledge-Based Systems 2021.
• Qiang Gao, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Fengli Zhang. 2021. Adversarial Human Trajectory Learning for Trip Recommendation. IEEE Transactions on Neural Networks and Learning Systems 2021.
• Qiang Gao, Goce Trajcevski, Fan Zhou, Kunpeng Zhang, Ting Zhong, and Fengli Zhang. 2019. DeepTrip: Adversarially Understanding Human Mobility for Trip Recommendation. Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL) 2019.
• Qiang Gao, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Fengli Zhang. 2019. Predicting Human mobility via Variational Attention. The World Wide Web Conference (WWW) 2019.
• Wang H, Shen H, Ouyang W, et al. 2018. Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence 2018.
• Feng S, Cong G, An B, et al 2022. POI2Vec: Geographical Latent Representation for Predicting Future Visitors. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
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• Zhao K, Zhang Y, Yin H, et al. 2020. Discovering Subsequence Patterns for Next POI Recommendation. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence 2020.
• Tang Q, Yang M, Yang Y. 2019. ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit. Journal of Advanced Transportation 2019.
• Dai S, Yu Y, Fan H, et al. 2022. Spatio-temporal representation learning with social tie for personalized POI recommendation. Data Science and Engineering 2022.
• Li M, Zheng W, Xiao Y, et al. 2020. An Adaptive POI Recommendation Algorithm by Integrating User’s Temporal and Spatial Features in LBSNs. Proceedings of the 3rd International Conference on Data Science and Information Technology 2020.
• Tahmasbi H, Jalali M, Shakeri H. 2021. Modeling user preference dynamics with coupled tensor factorization for social media recommendation. Journal of Ambient Intelligence and Humanized Computing 2021.
• Wang X, Sun G, Fang X, et al. 2022. Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation. IJCAI 2022.
• Li Y, Fan Z, Zhang J, et al. 2022. Heterogeneous hypergraph neural network for friend recommendation with human mobility. Proceedings of the 31st ACM International Conference on Information & Knowledge Management 2022.
• Pan X, Cai X, Song K, et al. 2023. Location Recommendation Based on Mobility Graph With Individual and Group Influences. IEEE Transactions on Intelligent Transportation Systems 2023.
• Wang Y, Tang S, Lei Y, et al. 2020. DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation. Proceedings of the 29th ACM International Conference on Information & Knowledge Management 2020.
• Liu X, Yang Y, Xu Y, et al. 2022. Real-time POI recommendation via modeling long- and short-term user preferences. Neurocomputing 2022.
• Wang Z, Zhu Y, Zhang Q, et al. 2022. Graph-Enhanced Spatial-Temporal Network for Next POI Recommendation. ACM Transactions on Knowledge Discovery from Data 2022.
• Li Q, Xu X, Liu X, et al. 2022. An attention-based spatiotemporal GGNN for next POI recommendation. IEEE Access 2022.
• Wang X, Fukumoto F, Li J, et al. 2023. STaTRL: Spatial-temporal and text representation learning for POI recommendation. Applied Intelligence 2023.
• Lang C, Wang Z, He K, et al. 2022. POI recommendation based on a multiple bipartite graph network model. The Journal of Supercomputing 2022.
• Wang K, Wang X, Lu X. 2023. POI recommendation method using LSTM-attention in LBSN considering privacy protection. Complex & Intelligent Systems 2023.
• Yan X, Song T, Jiao Y, et al. 2023. Spatio-temporal hypergraph learning for next POI recommendation. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023.
• Wang X, Fukumoto F, Cui J, et al. 2023. Eedn: Enhanced encoder-decoder network with local and global context learning for poi recommendation. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023.
• Fu J, Gao R, Yu Y, et al. 2024. Contrastive graph learning long and short-term interests for POI recommendation. Expert Systems with Applications 2024.
• Parveen R, Varma N S. 2021. Friend’s recommendation on social media using different algorithms of machine learning. Global Transitions Proceedings 2021.
• Saraswathi K, Mohanraj V, Suresh Y, et al. 2023. Deep Learning Enabled Social Media Recommendation Based on User Comments. Computer Systems Science and Engineering 2023.
• Liu H, Tong Y, Han J, et al. 2022. Incorporating Multi-Source Urban Data for Personalized and Context-Aware Multi-Modal Transportation Recommendation. IEEE Transactions on Knowledge and Data Engineering 2022.
• Zhu M, Hu J, Pu Z, et al. 2019. Personalized Context-Aware Multi-Modal Transportation Recommendation. arXiv: Computers and Society 2019.
• Dang W, Wang H, Pan S, et al. 2022. Predicting Human Mobility via Graph Convolutional Dual-attentive Networks. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining 2022.
• Fritz C, Dorigatti E, Rügamer D. 2022. Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany. Scientific Reports 2022.
• Niraula P, Mateu J, Chaudhuri S. 2022. A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases. Stochastic Environmental Research and Risk Assessment 2022.
• Liu Y, Rong Y, Guo Z, et al. 2023. Human mobility modeling during the COVID-19 pandemic via deep graph diffusion infomax. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
• Ma Y, Gerard P, Tian Y, et al. 2022. Hierarchical spatio-temporal graph neural networks for pandemic forecasting. Proceedings of the 31st ACM International Conference on Information & Knowledge Management 2022.
• Terroso-Sáenz F, Muñoz A. 2022. Nation-wide human mobility prediction based on graph neural networks. Applied Intelligence 2022.
• Kong X, Wang K, Hou M, et al. 2022. Exploring human mobility for multi-pattern passenger prediction: A graph learning framework. IEEE Transactions on Intelligent Transportation Systems 2022.
• Solatorio A V. 2023. GeoFormer: Predicting Human Mobility using Generative Pre-trained Transformer (GPT). Proceedings of the 1st International Workshop on the Human Mobility Prediction Challenge 2023.
• Choya T, Tamura N, Katayama S, et al. 2023. CrowdFlowTransformer: Capturing Spatio-Temporal Dependence for Forecasting Human Mobility. 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) 2023.
• Xu Z, Wei X, Hao J, et al. 2024. DGFormer: a physics-guided station level weather forecasting model with dynamic spatial-temporal graph neural network. GeoInformatica 2024.
• Lam R, Sanchez-Gonzalez A, Willson M, et al. 2022. GraphCast: Learning skillful medium-range global weather forecasting. arXiv preprint 2022.
• Pathak J, Subramanian S, Harrington P, et al. 2022. Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint 2022.
• Kurth T, Subramanian S, Harrington P, et al. 2023. Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators. Proceedings of the platform for advanced scientific computing conference 2023.
• Weyn J A, Durran D R, Caruana R, et al. 2021. Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models. Journal of Advances in Modeling Earth Systems 2021.
• Ni Q, Wang Y, Fang Y. 2022. GE-STDGN: a novel spatio-temporal weather prediction model based on graph evolution. Applied Intelligence 2022.
• Hu Y, Chen L, Wang Z, et al. 2023. SwinVRNN: A Data‐Driven Ensemble Forecasting Model via Learned Distribution Perturbation. Journal of Advances in Modeling Earth Systems 2023.
• Nguyen T, Brandstetter J, Kapoor A, et al. 2023. Climax: A foundation model for weather and climate. arXiv preprint 2023.
• Chen K, Han T, Gong J, et al. 2023. Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead. arXiv preprint 2023.
• Bi K, Xie L, Zhang H, et al. 2022. Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast. arXiv preprint 2022.
• Bi K, Xie L, Zhang H, et al. 2023. Accurate medium-range global weather forecasting with 3D neural networks. Nature 2023.

时空事件预测

• Hongzhu Fu, Fan Zhou, Qing Guo, Qiang Gao. 2024. Spatial-Temporal Augmentation for Crime Prediction (Student Abstract). The Thirty-Eighth AAAI Conference on Artificial Intelligence 2024.
• Qiang Gao, Hongzhu Fu, Yutao Wei, Li Huang, Xingmin Liu, and Guisong Liu. 2023. Spatial-Temporal Diffusion Probabilistic Learning for Crime Prediction. The 16th International Conference on Knowledge Science, Engineering and Management 2023.
• Xu L, Chen N, Chen Z, et al. 2021. Spatiotemporal forecasting in earth system science: Methods, uncertainties, predictability and future directions. Earth-Science Reviews 2021.
• Yang G, Yu H, Xi H. 2022. A Spatio-Temporal Traffic Flow Prediction Method Based on Dynamic Graph Convolution Network. 2022 34th Chinese Control and Decision Conference (CCDC). IEEE 2022.
• Cheng M, Jiang G P, Song Y, et al. 2022. Dynamic Spatio-temporal traffic flow prediction based on multi fusion graph attention network. 2022 41st Chinese Control Conference (CCC). IEEE 2022.
• Huang Z, Ma J, Dong Y, et al. 2022. Empowering Next POI Recommendation with Multi-Relational Modeling. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval 2022.
• Zhou B, Zhou H, Wang W, et al. 2024. HDM-GNN: A Heterogeneous Dynamic Multi-view Graph Neural Network for Crime Prediction. ACM Transactions on Sensor Networks 2024.
• Sun Y, Chen T, Yin H. 2023. Spatial-temporal meta-path guided explainable crime prediction. World Wide Web 2023.
• Liang W, Cao J, Chen L, et al. 2023. Crime prediction with missing data via spatiotemporal regularized tensor decomposition. IEEE Transactions on Big Data 2023.
• Wang C, Lin Z, Yang X, et al. 2022. Hagen: Homophily-aware graph convolutional recurrent network for crime forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
• Yang C. 2023. TransCrimeNet: A Transformer-Based Model for Text-Based Crime Prediction in Criminal Networks. arXiv preprint 2023.
• Hou M, Hu X, Cai J, et al. 2022. An Integrated Graph Model for Spatial–Temporal Urban Crime Prediction Based on Attention Mechanism. ISPRS International Journal of Geo-Information 2022.
• Gandapur M Q. 2022. E2E-VSDL: End-to-end video surveillance-based deep learning model to detect and prevent criminal activities. Image and Vision Computing 2022.
• Rayhan Y, Hashem T. 2023. AIST: An Interpretable Attention-Based Deep Learning Model for Crime Prediction. ACM Transactions on Spatial Algorithms and Systems 2023.
• Li Z, Huang C, Xia L, et al. 2022. Spatial-temporal hypergraph self-supervised learning for crime prediction. 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE 2022.
• Xia L, Huang C, Xu Y, et al. 2021. Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence 2021.
• Gao Q, Fu H, Wei Y, et al. 2023. Spatial-Temporal Diffusion Probabilistic Learning for Crime Prediction. International Conference on Knowledge Science, Engineering and Management. Cham: Springer Nature Switzerland 2023.
• Liu Y, Li K, Yan D, et al. 2023. The prediction of disaster risk paths based on IECNN model. Natural Hazards 2023.
• Eltehewy R, Abouelfarag A, Saleh S N. 2023. Efficient Classification of Imbalanced Natural Disasters Data Using Generative Adversarial Networks for Data Augmentation. ISPRS International Journal of Geo-Information 2023.
• Dikshit A, Pradhan B, Alamri A M. 2021. Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model. Science of The Total Environment 2021.
• Mokhtar A, Jalali M, He H, et al. 2021. Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms. IEEE Access 2021.
• Danandeh Mehr A, Rikhtehgar Ghiasi A, Yaseen Z M, et al. 2023. A novel intelligent deep learning predictive model for meteorological drought forecasting. Journal of Ambient Intelligence and Humanized Computing 2023.
• Zeng C, Bertsimas D. 2023. Global flood prediction: a multimodal machine learning approach. arXiv preprint 2023.
• Amato F, Guignard F, Robert S, et al. 2020. A novel framework for spatio-temporal prediction of environmental data using deep learning. Scientific reports 2020.
• Li T, Zhang J, Bao K, et al. 2020. Autost: Efficient neural architecture search for spatio-temporal prediction. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020.

时空数据补全

• Chen J, Chen P. 2017. A method based on tensor decomposition for missing multi-dimensional data completion. International Conference on Big Data 2017.
• Kong X, Zhou W, Shen G, et al. 2023. Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data. Knowledge-Based Systems 2023.
• Cai L, Sha C, He J, et al. 2023. Spatial–Temporal Data Imputation Model of Traffic Passenger Flow Based on Grid Division. ISPRS International Journal of Geo-Information 2023.
• Xu D, Peng H, Wei C, et al. 2021. Traffic state data imputation: An efficient generating method based on the graph aggregator. IEEE Transactions on Intelligent Transportation Systems 2021.
• Psychogyios K, Ilias L, Ntanos C, et al. 2023. Missing value imputation methods for electronic health records. IEEE Access 2023.
• Ye Y, Zhang S, Yu J J Q. 2021. Spatial-Temporal Traffic Data Imputation via Graph Attention Convolutional Network. Lecture Notes in Computer Science, Artificial Neural Networks and Machine Learning – ICANN 2021.
• Zhao D, Zhang Y, Wang W, et al. 2023. Car‐following trajectory data imputation with adversarial convolutional neural network. IET Intelligent Transport Systems 2023.
• Jiang L, Zhang T, Zuo Q, et al. 2022. A deep learning framework for traffic data imputation considering spatiotemporal dependencies. 2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE). IEEE 2022.
• Zhao C, Song A, Du Y, et al. 2022. TrajGAT: A map-embedded graph attention network for real-time vehicle trajectory imputation of roadside perception. Transportation research part C: emerging technologies 2022.
• Xu Y, Bazarjani A, Chi H, et al. 2023. Uncovering the missing pattern: Unified framework towards trajectory imputation and prediction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023.
• Qin K K, Ren Y, Shao W, et al. 2023. Multiple-level point embedding for solving human trajectory imputation with prediction. ACM Transactions on Spatial Algorithms and Systems 2023.
• Xu X, Lin M, Luo X, et al. 2023. HRST-LR: A Hessian Regularization Spatio-Temporal Low Rank Algorithm for Traffic Data Imputation. IEEE Transactions on Intelligent Transportation Systems 2023.

新智能时代的时空轨迹语义理解的挑战与机遇

• 王少华, 谢幸, 李勇, 等. 2024. 空间数据智能大模型研究--2024年中国空间数据智能战略发展白皮书.

多模态数据处理

• Lahat D, Adali T, Jutten C. 2015. Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects. Proceedings of the IEEE 2015.
• Shao R, Yang C, Li Q, et al. 2023. AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Thirteen Modalities. arXiv preprint 2023.
• Chen Y, Zhao M, Bruzzone L. 2023. Incomplete multimodal learning for remote sensing data fusion. arXiv preprint 2023.
• Min J H, Ham S W, Kim D K, et al. 2023. Deep Multimodal Learning for Traffic Speed Estimation Combining Dedicated Short-Range Communication and Vehicle Detection System Data. Transportation Research Record: Journal of the Transportation Research Board 2023.
• Xiao H, Zhao Y, Zhang H. 2022. Predict vessel traffic with weather conditions based on multimodal deep learning. Journal of Marine Science and Engineering 2022.
• Chen J, Zhang A. 2023. On Hierarchical Disentanglement of Interactive Behaviors for Multimodal Spatiotemporal Data with Incompleteness. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2023.
• Zhou B, Wang P, Wan J, et al. 2022. Decoupling and recoupling spatiotemporal representation for rgb-d-based motion recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.
• Zhao Y, Deng P, Liu J, et al. 2023. Causal conditional hidden Markov model for multimodal traffic prediction. Proceedings of the AAAI Conference on Artificial Intelligence 2023.

模型的透明可释

• Ma J, Zhou C, Cui P, et al. 2019. Learning Disentangled Representations for Recommendation. Neural Information Processing Systems 2019.
• Karpathy A, Johnson J, Fei-Fei L. 2015. Visualizing and understanding recurrent networks. arXiv preprint 2015.
• Hupkes D, Veldhoen S, Zuidema W. 2018. Visualisation and'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure. Journal of Artificial Intelligence Research 2018.
• Tenney I, Xia P, Chen B, et al. 2019. What do you learn from context? probing for sentence structure in contextualized word representations. arXiv preprint 2019.

开放语义建模

• Li Z, Xia L, Tang J, et al. 2024. Urbangpt: Spatio-temporal large language models. arXiv preprint 2024.
• Zhang S, Fu D, Liang W, et al. 2024. Trafficgpt: Viewing, processing and interacting with traffic foundation models. Transport Policy 2024.
• Guan Q, Ouyang J, Wu D, et al. 2024. CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System. arXiv preprint 2024.
• Singh A, Co-Reyes J D, Agarwal R, et al. 2023. Beyond human data: Scaling self-training for problem-solving with language models. arXiv preprint 2023.
• Liu R, Wei J, Liu F, et al. 2024. Best practices and lessons learned on synthetic data for language models. arXiv preprint 2024.
• Shumailov I, Shumaylov Z, Zhao Y, et al. 2023. The curse of recursion: Training on generated data makes models forget. arXiv preprint 2023.

模型可用与资源消耗

• Hu E J, Shen Y, Wallis P, et al. 2021. LoRA: Low-Rank Adaptation of Large Language Models. arXiv: Computation and Language 2021.
• Beltagy I, Peters M E, Cohan A. 2020. Longformer: The Long-Document Transformer. arXiv: Computation and Language 2020.
• Dai Z, Yang Z, Yang Y, et al. 2019. Transformer-XL: Attentive Language Models beyond a Fixed-Length Context. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019.
• Vadera S, Ameen S. 2022. Methods for Pruning Deep Neural Networks. IEEE Access 2022.

伦理与信任问题

• Morris J X, Kuleshov V, Shmatikov V, et al. 2023. Text embeddings reveal (almost) as much as text. arXiv preprint 2023.
• Garg N, Schiebinger L, Jurafsky D, et al. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences 2018.
• He J, Xia M, Fellbaum C, et al. 2022. MABEL: Attenuating gender bias using textual entailment data. arXiv preprint 2022.

常用数据集

分类类型	数据集名称	主要地区	数据集介绍
人类移动	GeoLife	亚洲	182个用户,17621个轨迹,91%的1~5S/P采样率
人类移动	TMD	意大利	13个用户,226个轨迹,0.05s/p采样率
人类移动	SHL	英国	3个用户,12个轨迹,1S/P采样率
人类移动	OpenStreetMap	全球	870万+轨迹,持续更新
人类移动	MDC	瑞士	185个轨迹,近200个个体
人类移动	MIT-Humob2023	日本	10万个人,85种场地
人类移动	COVlD19USFlows	美国	22万个场地,数百万匿名用户
人类移动	ETH walking pedestrians	瑞士	4006张图像,19个城市景观类别像素级注释
人类移动	US Covid-19	美国	记录和分析美国境内COVID-19疫情情况
人类移动	Italy Covid-19	意大利	记录和分析意大利境内COVID-19疫情情况
人类移动	Japan-Prefectures ILI	日本	关于日本各县流感病例统计
人类移动	US ILI	美国	2002年到2021年美国疾病控制和预防中心每周数据
交通轨迹	T-Drive	中国北京	10357辆车177 s/p(Avg.) 采样率
交通轨迹	Porto	葡萄牙波尔图	442辆车,1,710,990 条轨迹, 15 s/p 采样率
交通轨迹	Taxi-Shanghai	中国上海	4,316辆车,780万条轨迹, 5 s/p 采样率
交通轨迹	DiDi-Chengdu	中国成都	3,493,918条轨迹, 3 s/p Avg. 采样率
交通轨迹	DiDi-Xian	中国西安	2,180,348条轨迹, 3 s/p Avg. 采样率
交通轨迹	Greek	希腊雅典	50辆车, 1.100 条轨迹
交通轨迹	TaxiBJ	中国北京	30分钟间隔,34.000+辆出租车
交通轨迹	BikeNYC	美国纽约	1-小时间隔, 6,800+辆自行车
交通轨迹	TaxiBJ21	中国北京	30-分钟间隔,17,749 辆车
交通轨迹	California-PEMS	美国加利福尼亚	超过44681个检测器
交通轨迹	METR-LA	美国洛杉矶	1500个探测器,涵盖3420英里
交通轨迹	NYC taxi	美国纽约	2009-2018年的数据
交通轨迹	NYC bike	美国纽约	2013年到如今数据
交通轨迹	Chicago bike	美国芝加哥	2013到如今数据
交通轨迹	NYC accident	美国纽约	2012-2019交通事故
交通轨迹	Chicago accident	美国芝加哥	2001-2017年
兴趣元素	Gowalla	全球	196,591个节点, 950,327个边
兴趣元素	BrightKite	全球	58,228个节点, 214,078个边
兴趣元素	Foursquare-NK	美国纽约	38,336个场地, 824个用户
兴趣元素	Foursquare-TKY	日本	61,858个场地, 1,939个用户
兴趣元素	Foursquare-Global	全球	3,680,126个场地, 266,909个用户
兴趣元素	Weeplaces	全球	971,309个场地, 15,799个用户
兴趣元素	Yelp	全球	131,930个场地, 1,987,897个用户
兴趣元素	Instagram	美国纽约	13,187个场地, 78,233 个用户
兴趣元素	GMOVE	美国	72K条轨迹
兴趣元素	Mobike-Shanghai	中国上海	390K+ 辆自行车
兴趣元素	Bike-Xiamen	中国厦门	50K+ 辆自行车
兴趣元素	Citi Bikes	美国纽约	68K+ 辆自行车, 2,104个活动站
环境气象	HURDAT	大西洋	1,415 条轨迹, 6 h/p 采样率
环境气象	BousaiCrowd	日本	1百万个用户, 20报道/天采样率
环境气象	Beijing air quality	中国北京	检测站点为北京,2017-2018每天每小时的数据
环境气象	Shanghai air quality	中国上海	包含上海检测站点信息
环境气象	ERA5	全球	覆盖从1950年到现在
环境气象	Denmark wind speed	丹麦	包含丹麦境内多个检测站点数据
环境气象	Dutch wind speed	荷兰	包含荷兰境内风速数据,记录风速、风向、时间戳等
环境气象	Japan typhoon	日本	记录台风路径和轻度、降雨量、灾害影响等
环境气象	California earthquake	美国加利福尼亚	从1969年到2007年的地震信息