Unraveling the origin of exponential law in intra-urban human mobility

The vast majority of travel takes place within cities. Recently, new data has become available which allows for the discovery of urban mobility patterns which differ from established results about long distance travel. Specifically, the latest evidence increasingly points to exponential trip length distributions, contrary to the scaling laws observed on larger scales. In this paper, in order to explore the origin of the exponential law, we propose a new model which can predict individual flows in urban areas better. Based on the model, we explain the exponential law of intra-urban mobility as a result of the exponential decrease in average population density in urban areas. Indeed, both empirical and analytical results indicate that the trip length and the population density share the same exponential decaying rate.

[1]  Kazuyuki Aihara,et al.  Safety-Information-Driven Human Mobility Patterns with Metapopulation Epidemic Dynamics , 2012, Scientific Reports.

[2]  A. M. Edwards,et al.  Incorrect Likelihood Methods Were Used to Infer Scaling Laws of Marine Predator Search Behaviour , 2012, PloS one.

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

[4]  Cecilia Mascolo,et al.  A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.

[5]  Michael T. Gastner,et al.  The complex network of global cargo ship movements , 2010, Journal of The Royal Society Interface.

[6]  S. Phithakkitnukoon,et al.  Urban mobility study using taxi traces , 2011, TDMA '11.

[7]  Mark A. Miller,et al.  Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza , 2006, Science.

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

[9]  M. Y. Choi,et al.  Modification of the gravity model and application to the metropolitan Seoul subway system. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Soong Moon Kang,et al.  Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows , 2010, PloS one.

[11]  Hernán D. Rozenfeld,et al.  Laws of population growth , 2008, Proceedings of the National Academy of Sciences.

[12]  Tao Zhou,et al.  Origin of the scaling law in human mobility: hierarchy of traffic systems. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Harry Eugene Stanley,et al.  Calling patterns in human communication dynamics , 2013, Proceedings of the National Academy of Sciences.

[14]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

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

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

[17]  Tao Zhou,et al.  Diversity of individual mobility patterns and emergence of aggregated scaling laws , 2012, Scientific Reports.

[18]  Xiao-Pu Han,et al.  Diversity of Individual Mobility Patterns , 2012, ArXiv.

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

[20]  P. A. Prince,et al.  Lévy flight search patterns of wandering albatrosses , 1996, Nature.

[21]  Alessandro Vespignani,et al.  Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions , 2007, PLoS medicine.

[22]  John B. Parr,et al.  A Population-Density Approach to Regional Spatial Structure , 1985 .

[23]  Nuno,et al.  Exploratory Study of Urban Flow using Taxi Traces , 2011 .

[24]  Alessandro Vespignani,et al.  Multiscale mobility networks and the spatial spreading of infectious diseases , 2009, Proceedings of the National Academy of Sciences.

[25]  H. Stanley,et al.  Gravity model in the Korean highway , 2007, 0710.1274.

[26]  Chaoming Song,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[27]  Zengru Di,et al.  Toward a general understanding of the scaling laws in human and animal mobility , 2010, 1008.4394.

[28]  Tao Jia,et al.  An empirical study on human mobility and its agent-based modeling , 2012 .

[29]  Xiao Liang,et al.  The scaling of human mobility by taxis is exponential , 2011, ArXiv.

[30]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[31]  F. Calabrese,et al.  Urban gravity: a model for inter-city telecommunication flows , 2009, 0905.0692.

[32]  C. Clark Urban Population Densities , 1951 .

[33]  F. Weissing,et al.  Lévy Walks Evolve Through Interaction Between Movement and Environmental Complexity , 2011, Science.

[34]  R. Gallotti,et al.  Statistical laws in urban mobility from microscopic GPS data in the area of Florence , 2009, 0912.4371.

[35]  Alan Wilson,et al.  A statistical theory of spatial distribution models , 1967 .

[36]  Xing Xie,et al.  Urban computing with taxicabs , 2011, UbiComp '11.

[37]  David W. Sims,et al.  Levy flight search patterns of marine predators not questioned: a reply to Edwards et al , 2012, 1210.2288.

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

[39]  V. Jansen,et al.  Variation in individual walking behavior creates the impression of a Lévy flight , 2011, Proceedings of the National Academy of Sciences.

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

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

[42]  Vito Latora,et al.  Understanding mobility in a social petri dish , 2011, Scientific Reports.

[43]  Chaogui Kang,et al.  Intra-urban human mobility patterns: An urban morphology perspective , 2012 .

[44]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[45]  Nicolas E. Humphries,et al.  Environmental context explains Lévy and Brownian movement patterns of marine predators , 2010, Nature.