Fine-grained prediction of urban population using mobile phone location data

ABSTRACT Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model’s prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events.

[1]  Yoshua Bengio,et al.  Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.

[2]  Yu Zheng,et al.  Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.

[3]  Tian Lan,et al.  Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies , 2014 .

[4]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[5]  Carlo Ratti,et al.  Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network , 2009 .

[6]  R. Ahas,et al.  Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area: Case study with mobile positioning data , 2010 .

[7]  Carlo Ratti,et al.  Understanding individual mobility patterns from urban sensing data: A mobile phone trace example , 2013 .

[8]  Licia Capra,et al.  Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.

[9]  Ling Yin,et al.  Understanding the bias of call detail records in human mobility research , 2016, Int. J. Geogr. Inf. Sci..

[10]  Xiuwen Yi,et al.  DNN-based prediction model for spatio-temporal data , 2016, SIGSPATIAL/GIS.

[11]  Carlo Ratti,et al.  Exploring human movements in Singapore: a comparative analysis based on mobile phone and taxicab usages , 2013, UrbComp '13.

[12]  Wolfgang Lutz,et al.  The Future Population of the World: What Can We Assume Today? , 1995 .

[13]  Ambuj K. Singh,et al.  FCCF: forecasting citywide crowd flows based on big data , 2016, SIGSPATIAL/GIS.

[14]  Chaogui Kang,et al.  Social Sensing: A New Approach to Understanding Our Socioeconomic Environments , 2015 .

[15]  Wolfgang Lutz,et al.  The Future Population of the World: What Can We Assume Today? , 1995 .

[16]  Yi Zhu,et al.  Inferring individual daily activities from mobile phone traces: A Boston example , 2016 .

[17]  Qingquan Li,et al.  Another Tale of Two Cities: Understanding Human Activity Space Using Actively Tracked Cellphone Location Data , 2016, Geographies of Mobility.

[18]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[19]  Hassan A. Karimi,et al.  A novel methodology for prediction of spatial-temporal activities using latent features , 2017, Comput. Environ. Urban Syst..

[20]  Xinyue Ye,et al.  Editorial: human dynamics in the mobile and big data era , 2016, Int. J. Geogr. Inf. Sci..

[21]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[22]  Xuan Song,et al.  CityMomentum: an online approach for crowd behavior prediction at a citywide level , 2015, UbiComp.

[23]  Tao Zhang,et al.  Understanding Spatiotemporal Patterns of Human Convergence and Divergence Using Mobile Phone Location Data , 2016, ISPRS Int. J. Geo Inf..

[24]  C. Ratti,et al.  Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .

[25]  K C Land,et al.  Methods for national population forecasts: a review. , 1986, Journal of the American Statistical Association.

[26]  G. Carmichael.  Fundamentals of Demographic Analysis: Concepts, Measures and Methods , 2015 .

[27]  Yu Liu,et al.  Towards Estimating Urban Population Distributions from Mobile Call Data , 2012 .