Predicting Human Mobility from Region Functions

People in nowadays cities have been suffering from increasingly severe traffic jams due to the less awareness of how such collective human mobility are caused by urban planning. This study aims to discover the association between region functions and the resulting human mobility. We establish a linear regression model to predict the traffic flows of Beijing based on the input referred to as bags of POIs. By solving the predictor in the sense of sparse representation, we find that the average prediction precision is over 74% and each type of POI contributes differently in the predictor, which accounts for what factors and how such region functions attract people's visiting. Based on those findings, predictive human mobility could be taken into account when planning new regions and region functions.

[1]  Alexandre M. Bayen,et al.  Understanding Road Usage Patterns in Urban Areas , 2012, Scientific Reports.

[2]  Zhaohui Wu,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Land-Use Classification Using Taxi GPS Traces , 2022 .

[3]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[4]  Su Yang,et al.  Social context awareness from taxi traces: mining how human mobility patterns are shaped by bags of POI , 2015, UbiComp/ISWC Adjunct.

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

[6]  John W. Polak,et al.  Entangled communities and spatial synchronization lead to criticality in urban traffic , 2013, Scientific Reports.

[7]  Gang Pan,et al.  Mining the semantics of origin-destination flows using taxi traces , 2012, UbiComp '12.

[8]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[9]  Stefano Spaccapietra,et al.  Semantic trajectories: Mobility data computation and annotation , 2013, TIST.

[10]  Víctor Soto,et al.  Characterizing Urban Landscapes Using Geolocated Tweets , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[11]  Dietmar Bauer,et al.  Inferring land use from mobile phone activity , 2012, UrbComp '12.

[12]  Pietro Liò,et al.  Collective Human Mobility Pattern from Taxi Trips in Urban Area , 2012, PloS one.

[13]  Daniel Gatica-Perez,et al.  The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data , 2014, IEEE Transactions on Mobile Computing.

[14]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.