Characterizing the human mobility pattern in a large street network.

Previous studies demonstrated empirically that human mobility exhibits Lévy flight behavior. However, our knowledge of the mechanisms governing this Lévy flight behavior remains limited. Here we analyze over 72,000 people's moving trajectories, obtained from 50 taxicabs during a six-month period in a large street network, and illustrate that the human mobility pattern, or the Lévy flight behavior, is mainly attributed to the underlying street network. In other words, the goal-directed nature of human movement has little effect on the overall traffic distribution. We further simulate the mobility of a large number of random walkers and find that (1) the simulated random walkers can reproduce the same human mobility pattern, and (2) the simulated mobility rate of the random walkers correlates pretty well (an R square up to 0.87) with the observed human mobility rate.

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

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

[3]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

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

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

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

[7]  G. Viswanathan,et al.  Lévy flights and superdiffusion in the context of biological encounters and random searches , 2008 .

[8]  A. M. Edwards,et al.  Revisiting Lévy flight search patterns of wandering albatrosses, bumblebees and deer , 2007, Nature.

[9]  Nicolas E. Humphries,et al.  Scaling laws of marine predator search behaviour , 2008, Nature.

[10]  D. Sims,et al.  Minimizing errors in identifying Lévy flight behaviour of organisms. , 2007, The Journal of animal ecology.

[11]  R. Thomson,et al.  Bending the Axial Line : , 2003 .

[12]  M. Shlesinger,et al.  Beyond Brownian motion , 1996 .

[13]  Kyunghan Lee,et al.  On the Levy-Walk Nature of Human Mobility , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[14]  Bin Jiang,et al.  Street hierarchies: a minority of streets account for a majority of traffic flow , 2008, Int. J. Geogr. Inf. Sci..

[15]  Alessandro Flammini,et al.  Optimal traffic networks , 2006, ArXiv.

[16]  Michel L. Goldstein,et al.  Problems with fitting to the power-law distribution , 2004, cond-mat/0402322.

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

[18]  M. E. J. Newman,et al.  Power laws, Pareto distributions and Zipf's law , 2005 .

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

[20]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

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

[22]  H. Stanley,et al.  Optimizing the success of random searches , 1999, Nature.

[23]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[24]  Albert-László Barabási,et al.  The origin of bursts and heavy tails in human dynamics , 2005, Nature.

[25]  W. Stahel,et al.  Log-normal Distributions across the Sciences: Keys and Clues , 2001 .

[26]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[27]  D. Sornette,et al.  Stretched exponential distributions in nature and economy: “fat tails” with characteristic scales , 1998, cond-mat/9801293.