A Generative Model of Urban Activities from Cellular Data
暂无分享,去创建一个
Jean-François Paiement | Alexei Pozdnoukhov | Mogeng Yin | Madeleine Sheehan | Sidney Feygin | Jean-François Paiement | Mogeng Yin | Sidney A. Feygin | A. Pozdnoukhov | M. Sheehan
[1] Daniel Gatica-Perez,et al. A probabilistic approach to mining mobile phone data sequences , 2013, Personal and Ubiquitous Computing.
[2] Nirvana Meratnia,et al. A hierarchical hidden semi-Markov model for modeling mobility data , 2014, UbiComp.
[3] Davy Janssens,et al. Annotating mobile phone location data with activity purposes using machine learning algorithms , 2013, Expert Syst. Appl..
[4] Alexandre M. Bayen,et al. Understanding Road Usage Patterns in Urban Areas , 2012, Scientific Reports.
[5] Vincent Etter,et al. Where to go from here? Mobility prediction from instantaneous information , 2013, Pervasive Mob. Comput..
[6] Chandra R. Bhat,et al. A comprehensive daily activity-travel generation model system for workers , 2000 .
[7] Kentaro Toyama,et al. Project Lachesis: Parsing and Modeling Location Histories , 2004, GIScience.
[8] Licia Capra,et al. Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.
[9] Yingling Fan,et al. SmarTrAC: A Smartphone Solution for Context-Aware Travel and Activity Capturing , 2015 .
[10] Peter Widhalm,et al. Discovering urban activity patterns in cell phone data , 2015 .
[11] Margaret Martonosi,et al. Identifying Important Places in People's Lives from Cellular Network Data , 2011, Pervasive.
[12] A. Tatem,et al. Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.
[13] João Bártolo Gomes,et al. Where Will You Go? Mobile Data Mining for Next Place Prediction , 2013, DaWaK.
[14] S. Fienberg. An Iterative Procedure for Estimation in Contingency Tables , 1970 .
[15] M. Ben-Akiva,et al. Discrete choice models with latent choice sets , 1995 .
[16] Emilio Frazzoli,et al. A review of urban computing for mobile phone traces: current methods, challenges and opportunities , 2013, UrbComp '13.
[17] Xing Xie,et al. Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.
[18] Henry A. Kautz,et al. Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields , 2007, Int. J. Robotics Res..
[19] Petter Holme,et al. Predictability of population displacement after the 2010 Haiti earthquake , 2012, Proceedings of the National Academy of Sciences.
[20] A. Santos,et al. Summary of Travel Trends: 2009 National Household Travel Survey , 2011 .
[21] Zhe Zhu,et al. What's Your Next Move: User Activity Prediction in Location-based Social Networks , 2013, SDM.
[22] Henry A. Kautz,et al. Hierarchical Conditional Random Fields for GPS-Based Activity Recognition , 2005, ISRR.
[23] Francisco C. Pereira,et al. Activity Recognition for a Smartphone Based Travel Survey Based on Cross-User History Data , 2014, 2014 22nd International Conference on Pattern Recognition.
[24] Carlo Ratti,et al. Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data , 2013, PloS one.
[25] Daniel Gatica-Perez,et al. Discovering routines from large-scale human locations using probabilistic topic models , 2011, TIST.
[26] Jure Leskovec,et al. Friendship and mobility: user movement in location-based social networks , 2011, KDD.
[27] Ryosuke Shibasaki,et al. Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data , 2010, HBU.
[28] Yoshua Bengio,et al. An Input Output HMM Architecture , 1994, NIPS.
[29] Felix Kling,et al. When a city tells a story: urban topic analysis , 2012, SIGSPATIAL/GIS.
[30] Albert-László Barabási,et al. Understanding individual human mobility patterns , 2008, Nature.
[31] Albert-László Barabási,et al. Limits of Predictability in Human Mobility , 2010, Science.
[32] Kay W. Axhausen,et al. Agent-based simulation of travel demand: Structure and computational performance of MATSim-T , 2008 .