暂无分享,去创建一个
Bruno Lepri | Massimiliano Luca | Gianni Barlacchi | Luca Pappalardo | B. Lepri | L. Pappalardo | Gianni Barlacchi | Massimiliano Luca | Luca Pappalardo
[1] Jie Feng,et al. Learning to Simulate Human Mobility , 2020, KDD.
[2] Lantao Yu,et al. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.
[3] Angelo Facchini,et al. The Electric City as a Solution to Sustainable Urban Development , 2018 .
[4] Yan Wang,et al. Coupling sentiment and human mobility in natural disasters: a Twitter-based study of the 2014 South Napa Earthquake , 2018, Natural Hazards.
[5] Md Zakirul Alam Bhuiyan,et al. Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction , 2020, IEEE Transactions on Intelligent Transportation Systems.
[6] Daqing Zhang,et al. NationTelescope: Monitoring and visualizing large-scale collective behavior in LBSNs , 2015, J. Netw. Comput. Appl..
[7] Mark Dougherty,et al. SHORT TERM INTER-URBAN TRAFFIC FORECASTS USING NEURAL NETWORKS , 1997 .
[8] Luca Pappalardo,et al. A dataset to assess mobility changes in Chile following local quarantines , 2020, Scientific Data.
[9] Feng Liu,et al. Cross-City Transfer Learning for Deep Spatio-Temporal Prediction , 2018, IJCAI.
[10] Rong He,et al. Exploring AIS data for intelligent maritime routes extraction , 2020 .
[11] Clio Andris,et al. trajGANs : Using generative adversarial networks for geo-privacy protection of trajectory data ( Vision paper ) , 2018 .
[12] Mirco Musolesi,et al. Privacy and the City: User Identification and Location Semantics in Location-Based Social Networks , 2015, ICWSM.
[13] Billy M. Williams,et al. Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results , 2003, Journal of Transportation Engineering.
[14] Sébastien Gambs,et al. Show me how you move and I will tell you who you are , 2010, SPRINGL '10.
[15] Fei Wu,et al. HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction , 2018, IJCAI.
[16] Piotr Sapiezynski,et al. Evidence for a conserved quantity in human mobility , 2016, Nature Human Behaviour.
[17] Benoît Garbinato,et al. Generative Models for Simulating Mobility Trajectories , 2018, ArXiv.
[18] Jun Li,et al. Refined Taxi Demand Prediction with ST-Vec , 2018, 2018 26th International Conference on Geoinformatics.
[19] X. Ben,et al. Assessing the impact of coordinated COVID-19 exit strategies across Europe , 2020, Science.
[20] ANDREA HESS,et al. Data-driven Human Mobility Modeling , 2016, ACM Comput. Surv..
[21] Sonia Yeh,et al. From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data , 2019, EPJ Data Science.
[22] D. Hubel,et al. Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.
[23] Yoshua Bengio,et al. Attention-Based Models for Speech Recognition , 2015, NIPS.
[24] Marta C. González,et al. A universal model for mobility and migration patterns , 2011, Nature.
[25] Haoran Feng,et al. A Survey of Hybrid Deep Learning Methods for Traffic Flow Prediction , 2019, ICAIP.
[26] Guoqiang Peter Zhang,et al. Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.
[27] David S. Rosenblum,et al. A Non-Parametric Generative Model for Human Trajectories , 2018, IJCAI.
[28] Qing Li,et al. T-CONV: A Convolutional Neural Network for Multi-scale Taxi Trajectory Prediction , 2016, 2018 IEEE International Conference on Big Data and Smart Computing (BigComp).
[29] Liang Zhao,et al. Event Prediction in the Big Data Era , 2020, ACM Comput. Surv..
[30] Zbigniew Smoreda,et al. Unravelling daily human mobility motifs , 2013, Journal of The Royal Society Interface.
[31] Yanmin Zhu,et al. A Survey on Trajectory Data Mining: Techniques and Applications , 2016, IEEE Access.
[32] Marco De Nadai,et al. A multi-source dataset of urban life in the city of Milan and the Province of Trentino , 2015, Scientific Data.
[33] Balázs Csanád Csáji,et al. Exploring the Mobility of Mobile Phone Users , 2012, ArXiv.
[34] M. Barthelemy,et al. Human mobility: Models and applications , 2017, 1710.00004.
[35] Marc-Olivier Killijian,et al. Next place prediction using mobility Markov chains , 2012, MPM '12.
[36] Andrew J. Tatem,et al. WorldPop, open data for spatial demography , 2017, Scientific Data.
[37] Reza Shokri,et al. Synthesizing Plausible Privacy-Preserving Location Traces , 2016, 2016 IEEE Symposium on Security and Privacy (SP).
[38] Mirco Musolesi,et al. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis , 2015, UbiComp.
[39] Anna Monreale,et al. WhereNext: a location predictor on trajectory pattern mining , 2009, KDD.
[40] Andreas Vogelsang,et al. Destination Prediction Based on Partial Trajectory Data , 2020, 2020 IEEE Intelligent Vehicles Symposium (IV).
[41] B. G. Ratcliffe,et al. Short term traffic forecasting using time series methods , 1988 .
[42] Carlo Ratti,et al. Human mobility prediction based on individual and collective geographical preferences , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.
[43] Vartika Koolwal,et al. A comprehensive survey on trajectory-based location prediction , 2020, Iran Journal of Computer Science.
[44] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[45] Fabrizio Natale,et al. Mapping EU fishing activities using ship tracking data , 2016, ArXiv.
[46] Xing Xie,et al. GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory , 2010, IEEE Data Eng. Bull..
[47] Dino Pedreschi,et al. (So) Big Data and the transformation of the city , 2020, International Journal of Data Science and Analytics.
[48] D. Gática-Pérez,et al. Towards rich mobile phone datasets: Lausanne data collection campaign , 2010 .
[49] Aixin Sun,et al. A Survey of Location Prediction on Twitter , 2017, IEEE Transactions on Knowledge and Data Engineering.
[50] Lorenzo Mussone,et al. NEURAL-NETWORK MODELS FOR CLASSIFICATION AND FORECASTING OF FREEWAY TRAFFIC FLOW STABILITY , 1994 .
[51] Qing Yang,et al. GANs Based Density Distribution Privacy-Preservation on Mobility Data , 2018, Secur. Commun. Networks.
[52] Margaret Martonosi,et al. DP-WHERE: Differentially private modeling of human mobility , 2013, 2013 IEEE International Conference on Big Data.
[53] Daqing Zhang,et al. Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks , 2016, ACM Trans. Intell. Syst. Technol..
[54] Yi Liu,et al. Social media and mobility landscape: Uncovering spatial patterns of urban human mobility with multi source data , 2018, Frontiers of Environmental Science & Engineering.
[55] Xinning Zhu,et al. Deep multi-view residual attention network for crowd flows prediction , 2020, Neurocomputing.
[56] Luca Pappalardo,et al. Measuring objective and subjective well-being: dimensions and data sources , 2020, International Journal of Data Science and Analytics.
[57] Yu Zheng,et al. T-Drive trajectory data sample , 2011 .
[58] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[59] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[60] Takayuki Morikawa,et al. Big Trajectory Data Mining: A Survey of Methods, Applications, and Services , 2020, Sensors.
[61] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[62] Guanbin Li,et al. ACFM: A Dynamic Spatial-Temporal Network for Traffic Prediction , 2019, ArXiv.
[63] Ma Jun,et al. Research of Traffic Flow Forecasting Based on Neural Network , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.
[64] Jürgen Schmidhuber,et al. Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.
[65] Luca Pappalardo,et al. Gravity and scaling laws of city to city migration , 2018, PloS one.
[66] Andrew J. Tatem,et al. Mapping the denominator: spatial demography in the measurement of progress. , 2014, International health.
[67] Lidan Shou,et al. Splitter: Mining Fine-Grained Sequential Patterns in Semantic Trajectories , 2014, Proc. VLDB Endow..
[68] Xuan Song,et al. Prediction and Simulation of Human Mobility Following Natural Disasters , 2016, ACM Trans. Intell. Syst. Technol..
[69] Luca Pappalardo,et al. Data-driven generation of spatio-temporal routines in human mobility , 2016, Data Mining and Knowledge Discovery.
[70] Maria Riveiro,et al. Maritime anomaly detection: A review , 2018, WIREs Data Mining Knowl. Discov..
[71] Nuno R. Faria,et al. The effect of human mobility and control measures on the COVID-19 epidemic in China , 2020, Science.
[72] Vincent D. Blondel,et al. A survey of results on mobile phone datasets analysis , 2015, EPJ Data Science.
[73] Rahul Nair,et al. A Multi-Scale Approach to Data-Driven Mass Migration Analysis , 2016, SoGood@ECML-PKDD.
[74] A. Pentland,et al. Eigenbehaviors: identifying structure in routine , 2009, Behavioral Ecology and Sociobiology.
[75] Justin Carlson,et al. Mapping Large, Urban Environments with GPS-Aided SLAM , 2010 .
[76] Navdeep Jaitly,et al. Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.
[77] T. Gireeshkumar,et al. A Survey of Location Prediction Using Trajectory Mining , 2015 .
[78] Xuan Song,et al. DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction , 2018, AAAI.
[79] Giuliana Pallotta,et al. Maritime Traffic Networks: From Historical Positioning Data to Unsupervised Maritime Traffic Monitoring , 2018, IEEE Transactions on Intelligent Transportation Systems.
[80] Xuan Song,et al. A Variational Autoencoder Based Generative Model of Urban Human Mobility , 2019, 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).
[81] Qiang Gao,et al. Predicting Human Mobility via Variational Attention , 2019, WWW.
[82] Archan Misra,et al. Understanding the Interdependency of Land Use and Mobility for Urban Planning , 2018, UbiComp/ISWC Adjunct.
[83] Marta C. González,et al. Coupling human mobility and social ties , 2015, Journal of The Royal Society Interface.
[84] C. Ratti,et al. Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data , 2019, Journal of Exposure Science & Environmental Epidemiology.
[85] Luca Pappalardo,et al. Effective injury forecasting in soccer with GPS training data and machine learning , 2017, PloS one.
[86] Shwu-Jing Chang,et al. Vessel Traffic Analysis for Maritime Intelligent Transportation System , 2010, 2010 IEEE 71st Vehicular Technology Conference.
[87] Seungjae Shin,et al. User Mobility Synthesis based on Generative Adversarial Networks: A Survey , 2020, 2020 22nd International Conference on Advanced Communication Technology (ICACT).
[88] R. Gallotti,et al. Statistical laws in urban mobility from microscopic GPS data in the area of Florence , 2009, 0912.4371.
[89] Daniel B. Fambro,et al. Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting , 1999 .
[90] Yu Zheng,et al. Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks , 2019, IEEE Transactions on Knowledge and Data Engineering.
[91] Christian Szegedy,et al. DeepPose: Human Pose Estimation via Deep Neural Networks , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[92] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[93] L. Padma Suresh,et al. Artificial Intelligence and Evolutionary Algorithms in Engineering Systems , 2015 .
[94] João Gama,et al. Predicting Taxi–Passenger Demand Using Streaming Data , 2013, IEEE Transactions on Intelligent Transportation Systems.
[95] Chao Zhang,et al. SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories , 2017, CIKM.
[96] Alex Rutherford,et al. On the privacy-conscientious use of mobile phone data , 2018, Scientific Data.
[97] Wei Chen,et al. A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system , 2018, Neural Computing and Applications.
[98] Francesca Pratesi,et al. A Data Mining Approach to Assess Privacy Risk in Human Mobility Data , 2017, ACM Trans. Intell. Syst. Technol..
[99] Jure Leskovec,et al. Friendship and mobility: user movement in location-based social networks , 2011, KDD.
[100] Philippe Cudré-Mauroux,et al. Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach , 2019, WWW.
[101] Umair Qazi,et al. GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information , 2020, ACM SIGSPATIAL Special.
[102] Siddharth Gupta,et al. The TimeGeo modeling framework for urban mobility without travel surveys , 2016, Proceedings of the National Academy of Sciences.
[103] Zheng Hu,et al. Deep spatial-temporal networks for crowd flows prediction by dilated convolutions and region-shifting attention mechanism , 2020, Applied Intelligence.
[104] Ronaldo Menezes,et al. The effect of recency to human mobility , 2015, EPJ Data Science.
[105] Jimeng Sun,et al. RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism , 2016, NIPS.
[106] Jia Liu,et al. Urban flows prediction from spatial-temporal data using machine learning: A survey , 2019, ArXiv.
[107] Anna Monreale,et al. Modeling Adversarial Behavior Against Mobility Data Privacy , 2020, IEEE Transactions on Intelligent Transportation Systems.
[108] Jing Huang,et al. Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction , 2017, RecSys.
[109] Daqing Zhang,et al. Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[110] Franco Zambonelli,et al. On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors , 2017, Pervasive Mob. Comput..
[111] Luming Zhang,et al. GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media , 2016, KDD.
[112] Mirko Degli Esposti,et al. Entropic measures of individual mobility patterns , 2013 .
[113] Yu Zheng,et al. Trajectory Data Mining , 2015, ACM Trans. Intell. Syst. Technol..
[114] Gunnar Rätsch,et al. Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs , 2017, ArXiv.
[115] Chao Zhang,et al. DeepMove: Predicting Human Mobility with Attentional Recurrent Networks , 2018, WWW.
[116] Yong Li,et al. DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis , 2019, AAAI.
[117] Yunhe Feng,et al. Work from home during the COVID-19 pandemic: An observational study based on a large geo-tagged COVID-19 Twitter dataset (UsaGeoCov19) , 2020, Information Processing & Management.
[118] Y. Kamarianakis,et al. Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches , 2003 .
[119] Rafael Reuveny. Climate change-induced migration and violent conflict , 2007 .
[120] Pascal Vincent,et al. Artificial Neural Networks Applied to Taxi Destination Prediction , 2015, DC@PKDD/ECML.
[121] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[122] Tieniu Tan,et al. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts , 2016, AAAI.
[123] Yu Zheng,et al. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.
[124] S. C. Kremer,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[125] Piotr Sapiezynski,et al. Measuring Large-Scale Social Networks with High Resolution , 2014, PloS one.
[126] Zhu Xiao,et al. Stay of Interest: A Dynamic Spatiotemporal Stay Behavior Perception Method for Private Car Users , 2019, 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).
[127] Wen-Chih Peng,et al. Modeling User Mobility for Location Promotion in Location-based Social Networks , 2015, KDD.
[128] Chao Gao,et al. The temporal network of mobile phone users in Changchun Municipality, Northeast China , 2018, Scientific data.
[129] Gao Cong,et al. Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns , 2018, IJCAI.
[130] Ha Yoon Song,et al. Generating Human Mobility Route Based on Generative Adversarial Network , 2019, 2019 Federated Conference on Computer Science and Information Systems (FedCSIS).
[131] Massimiliano Luca,et al. Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information , 2020, ArXiv.
[132] Dino Pedreschi,et al. Understanding the patterns of car travel , 2013 .
[133] C. Gray,et al. Natural disasters and population mobility in Bangladesh , 2012, Proceedings of the National Academy of Sciences.
[134] Liang Liu,et al. Estimating Origin-Destination Flows Using Mobile Phone Location Data , 2011, IEEE Pervasive Computing.
[135] Jiajun Liu,et al. Understanding Human Mobility from Twitter , 2014, PloS one.
[136] Marco Fiore,et al. Complete trajectory reconstruction from sparse mobile phone data , 2019, EPJ Data Science.
[137] Marco Fiore,et al. Privacy in trajectory micro-data publishing: a survey , 2020, Trans. Data Priv..
[138] Huaiyu Wan,et al. Spatio-Temporal Recurrent Convolutional Networks for Citywide Short-term Crowd Flows Prediction , 2018, ICCDA.
[139] Camille Couprie,et al. Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[140] Marc Barthelemy,et al. A stochastic model of randomly accelerated walkers for human mobility , 2015, Nature Communications.
[141] Guangchun Luo,et al. Location prediction on trajectory data: A review , 2018, Big Data Min. Anal..
[142] P. Bajardi,et al. COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown , 2020, Scientific Data.
[143] Andrew J Tatem,et al. Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine. , 2019, Journal of travel medicine.
[144] Sabine Timpf,et al. Trajectory data mining: A review of methods and applications , 2016, J. Spatial Inf. Sci..
[145] Ge Chen,et al. A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes , 2019, Int. J. Geogr. Inf. Sci..
[146] Jiuxin Cao,et al. Survey on user location prediction based on geo-social networking data , 2020, World Wide Web.
[147] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[148] Jure Leskovec,et al. node2vec: Scalable Feature Learning for Networks , 2016, KDD.
[149] Thad Starner,et al. Learning Significant Locations and Predicting User Movement with GPS , 2002, Proceedings. Sixth International Symposium on Wearable Computers,.
[150] Filippo Simini,et al. scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data , 2019 .
[151] Corrado Moiso,et al. Anonymous or Not? Understanding the Factors Affecting Personal Mobile Data Disclosure , 2017, ACM Trans. Internet Techn..
[152] Thomas L. Martin,et al. A survey on predicting personal mobility , 2012, Int. J. Pervasive Comput. Commun..
[153] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[154] Chaoming Song,et al. Modelling the scaling properties of human mobility , 2010, 1010.0436.
[155] Andrea Belardinelli,et al. Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities , 2016 .
[156] Rosaldo J. F. Rossetti,et al. TwitterJam: Identification of mobility patterns in urban centers based on tweets , 2015, 2015 IEEE First International Smart Cities Conference (ISC2).
[157] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[158] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[159] Licia Capra,et al. Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.
[160] Luca Pappalardo,et al. Weak Nodes Detection in Urban Transport Systems: Planning for Resilience in Singapore , 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).
[161] Wei Li,et al. Densely Connected Convolutional Networks With Attention LSTM for Crowd Flows Prediction , 2019, IEEE Access.
[162] Imad Aad,et al. The Mobile Data Challenge: Big Data for Mobile Computing Research , 2012 .
[163] Gürkan Solmaz,et al. A Survey of Human Mobility Models , 2019, IEEE Access.
[164] Emilio Frazzoli,et al. A review of urban computing for mobile phone traces: current methods, challenges and opportunities , 2013, UrbComp '13.
[165] Dino Pedreschi,et al. The purpose of motion: Learning activities from Individual Mobility Networks , 2014, 2014 International Conference on Data Science and Advanced Analytics (DSAA).
[166] P. Young,et al. Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.
[167] Yiannis Kamarianakis,et al. Space-time modeling of traffic flow , 2002, Comput. Geosci..
[168] Stan Matwin,et al. Anomaly detection in maritime data based on geometrical analysis of trajectories , 2015, 2015 18th International Conference on Information Fusion (Fusion).
[169] Bruno Lepri,et al. Modeling Taxi Drivers' Behaviour for the Next Destination Prediction , 2018, ArXiv.
[170] Feng Xia,et al. Urban Human Mobility: Data-Driven Modeling and Prediction , 2019, SKDD.
[171] Dino Pedreschi,et al. Data science at SoBigData: the European research infrastructure for social mining and big data analytics , 2018, International Journal of Data Science and Analytics.
[172] Daqing Zhang,et al. PrivCheck: privacy-preserving check-in data publishing for personalized location based services , 2016, UbiComp.
[173] Fan Yang,et al. Optimizing Transportation Dynamics at a City-Scale Using a Reinforcement Learning Framework , 2020, IEEE Access.
[174] Yanming Shen,et al. A Comprehensive Survey on Traffic Prediction , 2020, ArXiv.
[175] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[176] Gao Cong,et al. Context-aware Deep Model for Joint Mobility and Time Prediction , 2020, WSDM.
[177] Siyuan Liu,et al. Urban human mobility data mining: An overview , 2016, 2016 IEEE International Conference on Big Data (Big Data).
[178] Mirco Musolesi,et al. Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors , 2017, EPJ Data Science.
[179] Víctor Soto,et al. Prediction of socioeconomic levels using cell phone records , 2011, UMAP'11.
[180] Asifullah Khan,et al. A survey of the recent architectures of deep convolutional neural networks , 2019, Artificial Intelligence Review.
[181] Dino Pedreschi,et al. Returners and explorers dichotomy in human mobility , 2015, Nature Communications.
[182] Xianfeng Tang,et al. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction , 2018, AAAI.
[183] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..
[184] Wanggen Wan,et al. Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis , 2019, ISPRS Int. J. Geo Inf..
[185] Cecilia Mascolo,et al. A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.
[186] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[187] Marco De Nadai,et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle , 2020, Science Advances.
[188] Zbigniew Smoreda,et al. An analytical framework to nowcast well-being using mobile phone data , 2016, International Journal of Data Science and Analytics.
[189] Paolo Rosso,et al. Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States! , 2020, IJCAI.
[190] Albert-László Barabási,et al. Understanding individual human mobility patterns , 2008, Nature.
[191] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[192] T. Geisel,et al. The scaling laws of human travel , 2006, Nature.
[193] Albert-László Barabási,et al. Limits of Predictability in Human Mobility , 2010, Science.
[194] Alan M. MacEachren,et al. Geo-Located Tweets. Enhancing Mobility Maps and Capturing Cross-Border Movement , 2015, PloS one.
[195] Marco Conti,et al. Human mobility models for opportunistic networks , 2011, IEEE Communications Magazine.
[196] Alessandro Moschitti,et al. Structural Semantic Models for Automatic Analysis of Urban Areas , 2017, ECML/PKDD.
[197] Yongjian Yang,et al. Inter-urban mobility via cellular position tracking in the southeast Songliao Basin, Northeast China , 2019, Scientific Data.
[198] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[199] Ciro Cattuto,et al. An individual-level ground truth dataset for home location detection , 2020, ArXiv.