Urban Dynamic Estimation Using Mobile Phone Logs and Locally Varying Anisotropy

In telecommunications, the billing data of each telephone, denoted call detail records (CDRs), are a large and rich database with information that can be geo-located. By analyzing the events logged in each antenna, a set of time series can be constructed measuring the number of voice and data events in each time of the day. One question that can be addressed using these data involves estimating the movement or flow of people in the city, which can be used for prediction and monitoring in transportation or urban planning. In this work, geostatistical estimation techniques such as kriging and inverse distance weighting (IDW) are used to numerically estimate the flow of people. In order to improve the accuracy of the model, secondary information is included in the estimation. This information represents the locally varying anisotropy (LVA) field associated with the major streets and roads in the city. By using this technique, the flow estimation can be obtained with a better quantitative and qualitative interpretation. In terms of storage and computing power, the volume of raw information is extremely large; for that reason big data technologies are mandatory to query the database. Additionally, if high-resolution grids are used in the estimation, high-performance computing techniques are necessary to speed up the numerical computations using LVA codes. Case studies are shown, using voice/data records from anonymized clients of Telefonica Movistar in Santiago, capital of Chile.

[1]  R. Shibasaki,et al.  An Implementation of Mobile Sensing for Large-Scale Urban Monitoring , 2008 .

[2]  Carlo Curino,et al.  Apache Tez: A Unifying Framework for Modeling and Building Data Processing Applications , 2015, SIGMOD Conference.

[3]  Olof Görnerup,et al.  Scalable Mining of Common Routes in Mobile Communication Network Traffic Data , 2012, Pervasive.

[4]  Aleksandar Matic,et al.  Mobile Network Data for Public Health: Opportunities and Challenges , 2015, Front. Public Health.

[5]  Jeff B. Boisvert,et al.  Inference of locally varying anisotropy fields from diverse data sources , 2015, Comput. Geosci..

[6]  Rade Stanojevic,et al.  From Cells to Streets: Estimating Mobile Paths with Cellular-Side Data , 2014, CoNEXT.

[7]  Marta C. González,et al.  Analyzing Cell Phone Location Data for Urban Travel , 2015 .

[8]  Carlo Ratti,et al.  Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome , 2011, IEEE Transactions on Intelligent Transportation Systems.

[9]  Scott Shenker,et al.  Spark: Cluster Computing with Working Sets , 2010, HotCloud.

[10]  Clayton V. Deutsch,et al.  Kriging in the Presence of Locally Varying Anisotropy Using Non-Euclidean Distances , 2009 .

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

[12]  G. Madey,et al.  Uncovering individual and collective human dynamics from mobile phone records , 2007, 0710.2939.

[13]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[14]  Julián M. Ortiz,et al.  Acceleration of the Geostatistical Software Library (GSLIB) by code optimization and hybrid parallel programming , 2015, Comput. Geosci..

[15]  Clayton V. Deutsch,et al.  Programs for kriging and sequential Gaussian simulation with locally varying anisotropy using non-Euclidean distances , 2011, Comput. Geosci..

[16]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[17]  Víctor Soto,et al.  Prediction of socioeconomic levels using cell phone records , 2011, UMAP'11.

[18]  Rohit Chandra,et al.  Parallel programming in openMP , 2000 .

[19]  Songwu Lu,et al.  Instability in Distributed Mobility Management: Revisiting Configuration Management in 3G/4G Mobile Networks , 2016, SIGMETRICS.

[20]  Arnaud Browet,et al.  Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[21]  Antonio Lima,et al.  Understanding individual routing behaviour , 2016, Journal of The Royal Society Interface.

[22]  Pavan Holla,et al.  Route detection and mobility based clustering , 2011, 2011 IEEE 5th International Conference on Internet Multimedia Systems Architecture and Application.

[23]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[24]  Marta C. González,et al.  The path most traveled: Travel demand estimation using big data resources , 2015, Transportation Research Part C: Emerging Technologies.