Growing Spatially Embedded Social Networks for Activity-Travel Analysis Based on Artificial Transportation Systems

Social activity-travel has gained more and more attention as it is a growing percentage of the whole travel. To study its generation mechanism and behavioral characteristics, social network data are usually essential. However, due to individual privacy, it is rather difficult for traditional methods such as questionnaires to collect abundant reliable data. Therefore, we propose a novel method to grow realistic social networks based on artificial transportation systems (ATS). By incorporating the activity-travel simulation provided by ATS and a new agent-based model for social interaction, the method takes into account human mobility to generate spatially embedded social networks. Human mobility shapes and impacts social networks dynamically but is usually ignored by related studies. A case study based on computational experiments is carried out to verify the method. The results indicate that the method can generate social networks with similar topological and spatial characteristics to real social networks.

[1]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[2]  Raghu Pasupathy.  Generating Homogeneous Poisson Processes , 2011 .

[3]  Wang Feiyue,et al.  Parallel system methods for management and control of complex systems , 2004 .

[4]  Stefan Bornholdt,et al.  Emergence of a small world from local interactions: modeling acquaintance networks. , 2002, Physical review letters.

[5]  Gunnar Flötteröd,et al.  A model for spatially embedded social networks , 2009 .

[6]  T. Arentze,et al.  Social Networks, Social Interactions, and Activity-Travel Behavior: A Framework for Microsimulation , 2008 .

[7]  A. Arenas,et al.  Models of social networks based on social distance attachment. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Bart De Schutter,et al.  IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Editor-In-Chief , 2005 .

[9]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[10]  Harry Timmermans,et al.  Modeling Social Networks in Geographic Space: Approach and Empirical Application , 2012 .

[11]  B. Wellman,et al.  How Far and with Whom Do People Socialize? , 2008 .

[12]  Wang Feiyue,et al.  From Artificial Life to Artificial Societies——New Methods for Studies of Complex Social Systems , 2004 .

[13]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[14]  K. Nagel,et al.  Insights into a spatially embedded social network from a large-scale snowball sample , 2011 .

[15]  Stanley Milgram,et al.  An Experimental Study of the Small World Problem , 1969 .

[16]  K. Axhausen.  Social Networks, Mobility Biographies, and Travel: Survey Challenges , 2008 .

[17]  Yves Rozenholc,et al.  How many bins should be put in a regular histogram , 2006 .

[18]  M Girvan,et al.  Structure of growing social networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  P. Lazarsfeld,et al.  Friendship as Social process: a substantive and methodological analysis , 1964 .

[20]  Philippe De Wilde,et al.  Analysis and identification of a social interaction model , 2005 .

[21]  H. Herrmann,et al.  Agent-based model for friendship in social networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Ulrik Brandes,et al.  Social Networks , 2013, Handbook of Graph Drawing and Visualization.

[23]  Wang Fei.  Population Studies and Artificial Societies: A Discussion of Artificial Population Systems and Their Applications , 2005 .

[24]  R. Rossetti,et al.  Activity-Based Analysis of Travel Demand Using Cognitive Agents , 2005 .

[25]  Garry Robins,et al.  A spatial model for social networks , 2006 .

[26]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[27]  Jin Zhou,et al.  Scale-free networks which are highly assortative but not small world. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Wenji Mao,et al.  Social Computing: From Social Informatics to Social Intelligence , 2007, IEEE Intell. Syst..

[29]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[30]  Bart De Schutter,et al.  A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[31]  W. G. Mueller.  Dynamic Network Formation with Reinforcement Learning , 2012 .

[32]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[33]  Bill Mitchell,et al.  Networks and geography: Modelling community network structures as the outcome of both spatial and network processes , 2012, Soc. Networks.

[34]  Sara S. Metcalf,et al.  Spatial Dynamics of Social Network Evolution , 2005 .

[35]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[36]  Miklós Telek,et al.  Analysis of Large Scale Interacting Systems by Mean Field Method , 2008, 2008 Fifth International Conference on Quantitative Evaluation of Systems.

[37]  Dino Pedreschi,et al.  Human mobility, social ties, and link prediction , 2011, KDD.

[38]  Fei-Yue Wang,et al.  Artificial Societies for Integrated and Sustainable Development of Metropolitan Systems , 2004, IEEE Intell. Syst..

[39]  Injong Rhee,et al.  On the levy-walk nature of human mobility , 2011, TNET.

[40]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[41]  Lynne Hamill,et al.  Social Circles: A Simple Structure for Agent-Based Social Network Models , 2009, J. Artif. Soc. Soc. Simul..

[42]  Eric J Miller,et al.  Collecting Social Network Data to Study Social Activity-Travel Behavior: An Egocentric Approach , 2008 .

[43]  Fei-Yue Wang,et al.  Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications , 2010, IEEE Transactions on Intelligent Transportation Systems.

[44]  Frank S. Koppelman,et al.  Modeling household activity–travel interactions as parallel constrained choices , 2005 .

[45]  Beom Jun Kim,et al.  Growing scale-free networks with tunable clustering. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[46]  Fei-Yue Wang,et al.  Toward a Paradigm Shift in Social Computing: The ACP Approach , 2007, IEEE Intell. Syst..

[47]  E. Todeva.  Networks , 2007 .

[48]  Morroe Berger,et al.  Freedom and control in modern society , 1954 .

[49]  Juan Antonio Carrasco,et al.  Network Capital, Social Networks, and Travel: An Empirical Illustration from Concepción, Chile , 2012 .

[50]  Zhou Tao.  Researches of Human Dynamics , 2010 .

[51]  Kay W. Axhausen,et al.  An agent-based random-utility-maximization model to generate social networks with transitivity in geographic space , 2013, Soc. Networks.

[52]  H. Timmermans,et al.  Size and Composition of Ego-Centered Social Networks and Their Effect on Geographic Distance and Contact Frequency , 2009 .

[53]  Juan Pavón,et al.  Friends Forever: Social Relationships with a Fuzzy Agent-Based Model , 2008, HAIS.

[54]  Ta Theo Arentze,et al.  Modeling social interactions between individuals for joint activity scheduling , 2012 .

[55]  van den P.E.W. Berg,et al.  The spatiality of personal networks in four countries : a comparative study , 2012 .

[56]  Kun Zhao,et al.  Social Interactions Model and Adaptability of Human Behavior , 2011, Front. Physio..

[57]  W. Marsden.  I and J , 2012 .

[58]  Fenghua Zhu,et al.  Computational Traffic Experiments Based on Artificial Transportation Systems: An Application of ACP Approach , 2013, IEEE Transactions on Intelligent Transportation Systems.

[59]  William B. Walstad,et al.  Economics: Principles, Problems, and Policies , 2012 .

[60]  Paul Erdös,et al.  On random graphs, I , 1959 .