Inferring modes of transportation using mobile phone data
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[1] Brenton M. Wiernik,et al. Does perceived stress mediate the relationship between commuting and health-related quality of life? , 2017 .
[2] Vladia Pinheiro,et al. Micro-interventions in urban transportation from pattern discovery on the flow of passengers and on the bus network , 2016, 2016 IEEE International Smart Cities Conference (ISC2).
[3] Karthik Devarajan,et al. Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology , 2008, PLoS Comput. Biol..
[4] Laura Ferrari,et al. Urban Sensing Using Mobile Phone Network Data: A Survey of Research , 2014, ACM Comput. Surv..
[5] J. D. Whyatt,et al. Line generalisation by repeated elimination of points , 1993 .
[6] Vincent D. Blondel,et al. A survey of results on mobile phone datasets analysis , 2015, EPJ Data Science.
[7] C. Ding,et al. On the Equivalence of Nonnegative Matrix Factorization and K-means - Spectral Clustering , 2005 .
[8] Rossano Schifanella,et al. The Digital Life of Walkable Streets , 2015, WWW.
[9] M. Hilbert,et al. Big Data for Development: A Review of Promises and Challenges , 2016 .
[10] Matthew E. Kahn,et al. Why do the poor live in cities? The role of public transportation ✩ , 2008 .
[11] Leo Ferres,et al. The effect of Pokémon Go on the pulse of the city: a natural experiment , 2016, EPJ Data Science.
[12] Pablo Guarda,et al. What is behind fare evasion in urban bus systems? An econometric approach , 2016 .
[13] Antonio Gschwender,et al. Transantiago: A tale of two cities , 2007 .
[14] Glenn Lyons,et al. A Human Perspective on the Daily Commute: Costs, Benefits and Trade‐offs , 2008 .
[15] Simon Washington,et al. Shortest path and vehicle trajectory aided map-matching for low frequency GPS data , 2015 .
[16] Vanessa Frías-Martínez,et al. Estimation of urban commuting patterns using cellphone network data , 2012, UrbComp '12.
[17] H. Sebastian Seung,et al. Algorithms for Non-negative Matrix Factorization , 2000, NIPS.
[18] Carlo Ratti,et al. Transportation mode inference from anonymized and aggregated mobile phone call detail records , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.
[19] Nathan Eagle,et al. Limits of Predictability in Commuting Flows in the Absence of Data for Calibration , 2014, Scientific Reports.
[20] Albert-László Barabási,et al. Understanding individual human mobility patterns , 2008, Nature.
[21] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[22] Pietro Liò,et al. Collective Human Mobility Pattern from Taxi Trips in Urban Area , 2012, PloS one.
[23] Carlo Ratti,et al. Geo-located Twitter as proxy for global mobility patterns , 2013, Cartography and geographic information science.
[24] M Bayen Alexandre,et al. Negative externalities of GPS-enabled routing applications: A game theoretical approach , 2016 .
[25] Felipe González,et al. A combined destination and route choice model for a bicycle sharing system , 2016 .
[26] Xin Liu,et al. Document clustering based on non-negative matrix factorization , 2003, SIGIR.
[27] Mahmoud Mesbah,et al. Validating and improving public transport origin–destination estimation algorithm using smart card fare data ☆ , 2016 .
[28] M. Batty,et al. Gravity versus radiation models: on the importance of scale and heterogeneity in commuting flows. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[29] R. Walgate. Tale of two cities , 1984, Nature.
[30] Alejandro Tirachini,et al. Estimation of travel time variability for cars, buses, metro and door-to-door public transport trips in Santiago, Chile , 2016 .
[31] D. Sculley,et al. Web-scale k-means clustering , 2010, WWW '10.
[32] Ricardo Baeza-Yates,et al. Modern Information Retrieval - the concepts and technology behind search, Second edition , 2011 .
[33] Edward F. McQuarrie,et al. Focus Groups: Theory and Practice , 1991 .
[34] Shai Ben-David,et al. New England , 1894, Letters from America.
[35] K. Selçuk Candan,et al. GI-NMF: Group Incremental Non-Negative Matrix Factorization on Data Streams , 2014, CIKM.
[36] D. Kahneman,et al. A Survey Method for Characterizing Daily Life Experience: The Day Reconstruction Method , 2004, Science.
[37] Brian E. Granger,et al. IPython: A System for Interactive Scientific Computing , 2007, Computing in Science & Engineering.
[38] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[39] Liang Liu,et al. Estimating Origin-Destination Flows Using Mobile Phone Location Data , 2011, IEEE Pervasive Computing.
[40] Masao Kuwahara,et al. Estimating origin-destination matrices from roadside survey data , 1987 .
[41] Chris H. Q. Ding,et al. Symmetric Nonnegative Matrix Factorization for Graph Clustering , 2012, SDM.
[42] Ciro Cattuto,et al. Predicting human mobility through the assimilation of social media traces into mobility models , 2016, EPJ Data Science.
[43] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[44] Andrzej Cichocki,et al. Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations , 2009, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..
[45] Ciro Cattuto,et al. Shopping mall attraction and social mixing at a city scale , 2018, EPJ Data Science.
[46] S. O’sullivan,et al. Walking Distances to and from Light-Rail Transit Stations , 1996 .
[47] Marta C. González,et al. Origin-destination trips by purpose and time of day inferred from mobile phone data , 2015 .
[48] Skipper Seabold,et al. Statsmodels: Econometric and Statistical Modeling with Python , 2010, SciPy.
[49] J. Cullum,et al. A Lanczos Algorithm for Computing Singular Values and Vectors of Large Matrices , 1983 .
[50] G. Madey,et al. Uncovering individual and collective human dynamics from mobile phone records , 2007, 0710.2939.
[51] E. Cascetta. Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator , 1984 .
[52] Wes McKinney,et al. Data Structures for Statistical Computing in Python , 2010, SciPy.
[53] R. Groves. Nonresponse Rates and Nonresponse Bias in Household Surveys , 2006 .
[54] Xing Xie,et al. Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.
[55] Pu Wang,et al. Development of origin–destination matrices using mobile phone call data , 2014 .
[56] James D. Wilson,et al. Topic supervised non-negative matrix factorization , 2017, ArXiv.
[57] Randolph W. Hall,et al. Handbook of transportation science , 1999 .
[58] Eduardo Graells-Garrido,et al. Sensing Urban Patterns with Antenna Mappings: The Case of Santiago, Chile , 2016, Sensors.
[59] Eduardo Graells-Garrido,et al. A Day of Your Days: Estimating Individual Daily Journeys Using Mobile Data to Understand Urban Flow , 2016, Urb-IoT.
[60] Susan Parham,et al. Happy city: transforming our lives through urban design , 2014 .
[61] Hjp Harry Timmermans,et al. Transportation mode recognition using GPS and accelerometer data , 2013 .
[62] Yukiko Kawai,et al. Twitter-based Urban Area Characterization by Non-negative Matrix Factorization , 2015, BigDAS.
[63] T. Arentze,et al. Travelers’ Preferences in Multimodal Networks: Design and Results of a Comprehensive Series of Choice Experiments , 2013 .
[64] Scott A. Hale,et al. Estimating local commuting patterns from geolocated Twitter data , 2017, EPJ Data Science.
[65] Éric Gaussier,et al. Relation between PLSA and NMF and implications , 2005, SIGIR '05.
[66] Longbing Cao,et al. Data Science , 2017, ACM Comput. Surv..
[67] Eduardo Graells-Garrido,et al. Toward Finding Latent Cities , 2018, IUI Workshops.
[68] Pu Wang,et al. Transportation Mode Split with Mobile Phone Data , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.
[69] Kees Maat,et al. Day-to-Day Choice to Commute or Not by Bicycle , 2011 .
[70] Asha Weinstein Agrawal,et al. How Far, by Which Route and Why? A Spatial Analysis of Pedestrian Preference , 2007 .
[71] Marcela Munizaga,et al. Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile , 2012 .
[72] Haesun Park,et al. Toward Faster Nonnegative Matrix Factorization: A New Algorithm and Comparisons , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[73] Hesham A. Rakha,et al. Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data , 2015, IEEE Transactions on Intelligent Transportation Systems.