Agent-based simulation of human movement shaped by the underlying street structure

Relying on random and purposive moving agents, we simulated human movement in large street networks. We found that aggregate flow, assigned to individual streets, is mainly shaped by the underlying street structure, and that human moving behavior (either random or purposive) has little effect on the aggregate flow. This finding implies that given a street network, the movement patterns generated by purposive walkers (mostly human beings) and by random walkers are the same. Based on the simulation and correlation analysis, we further found that the closeness centrality is not a good indicator for human movement, in contrast to a long-standing view held by space syntax researchers. Instead we suggest that Google's PageRank and its modified version (weighted PageRank), betweenness and degree centralities are all better indicators for predicting aggregate flow.

[1]  Bin Jiang,et al.  Ranking spaces for predicting human movement in an urban environment , 2006, Int. J. Geogr. Inf. Sci..

[2]  David Banister,et al.  Configurational Modelling of Urban Movement Networks , 1998 .

[3]  Alasdair Turner,et al.  The role of angularity in route choice: an analysis of motorcycle courier GPS traces , 2009, COSIT 2009.

[4]  Alasdair Turner,et al.  From Axial to Road-Centre Lines: A New Representation for Space Syntax and a New Model of Route Choice for Transport Network Analysis , 2007 .

[5]  Alasdair Turner.  The Role of Angularity in Route Choice , 2009, COSIT.

[6]  R. Thomson,et al.  Smoothly continuous road centre-line segments as a basis for road network analysis , 2003 .

[7]  A Turner.  To move through space: lines of vision and movement , 2007 .

[8]  Amy Nicole Langville,et al.  Google's PageRank and beyond - the science of search engine rankings , 2006 .

[9]  Lucas Figueiredo,et al.  Continuity lines in the axial system , 2005 .

[10]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[11]  B. Hillier,et al.  The Social Logic of Space , 1984 .

[12]  Bin Jiang,et al.  Street‐based topological representations and analyses for predicting traffic flow in GIS , 2007, Int. J. Geogr. Inf. Sci..

[13]  Alan Penn,et al.  The architecture of society: stochastic simulation of urban movement , 1994 .

[14]  Bill Hillier,et al.  Network and Psychological Effects in Urban Movement , 2005, COSIT.

[15]  Bin Jiang,et al.  Self-organized natural roads for predicting traffic flow: a sensitivity study , 2008, 0804.1630.

[16]  P. Ball.  Critical Mass: How One Thing Leads to Another , 2004 .

[17]  Alan Penn,et al.  Natural Movement: Or, Configuration and Attraction in Urban Pedestrian Movement , 1993 .

[18]  B. Jiang.  A topological pattern of urban street networks: Universality and peculiarity , 2007, physics/0703223.

[19]  Bill Hillier,et al.  The Hidden Geometry of Deformed Grids: Or, Why Space Syntax Works, When it Looks as Though it Shouldn't , 1999 .

[20]  Bill Hillier,et al.  Space is the machine: A configurational theory of architecture , 1996 .

[21]  Wenpu Xing,et al.  Weighted PageRank algorithm , 2004, Proceedings. Second Annual Conference on Communication Networks and Services Research, 2004..

[22]  Mark Buchanan,et al.  The Social Atom: Why the Rich Get Richer, Cheaters Get Caught, and Your Neighbor Usually Looks Like You , 2007 .

[23]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[24]  Bin Jiang,et al.  Characterizing the human mobility pattern in a large street network. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Adrian Kuhn.  Collective Behavior , 2007 .

[26]  Christophe Claramunt,et al.  Topological Analysis of Urban Street Networks , 2004 .

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