Entangled communities and spatial synchronization lead to criticality in urban traffic

Understanding the relation between patterns of human mobility and the scaling of dynamical features of urban environments is a great importance for today's society. Although recent advancements have shed light on the characteristics of individual mobility, the role and importance of emerging human collective phenomena across time and space are still unclear. In this Article, we show by using two independent data-analysis techniques that the traffic in London is a combination of intertwined clusters, spanning the whole city and effectively behaving as a single correlated unit. This is due to algebraically decaying spatio-temporal correlations, that are akin to those shown by systems near a critical point. We describe these correlations in terms of Taylor's law for fluctuations and interpret them as the emerging result of an underlying spatial synchronisation. Finally, our results provide the first evidence for a large-scale spatial human system reaching a self-organized critical state.

[1]  Kay W. Axhausen,et al.  Analysis of driver's response to real-time information in Switzerland , 2006 .

[2]  Asad J. Khattak,et al.  Role of travel information in supporting travel decision adaption: exploring spatial patterns , 2013 .

[3]  P. Mucha,et al.  Spectral tripartitioning of networks. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  C. Daganzo,et al.  Macroscopic relations of urban traffic variables: Bifurcations, multivaluedness and instability , 2011 .

[5]  André de Palma,et al.  Dynamic and Static congestion models: A review , 2010 .

[6]  N. Geroliminis,et al.  Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings - eScholarship , 2007 .

[7]  Bo Chen,et al.  A Review of the Applications of Agent Technology in Traffic and Transportation Systems , 2010, IEEE Transactions on Intelligent Transportation Systems.

[8]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[9]  B D Greenshields,et al.  A study of traffic capacity , 1935 .

[10]  Bing-Hong Wang,et al.  Weighted congestion coefficient feedback in intelligent transportation systems , 2010 .

[11]  Gustav Pomberger,et al.  Self-Organization in Trafric Networks by Digital Pheromones , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[12]  Danny Weyns,et al.  A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems , 2011, IEEE Transactions on Intelligent Transportation Systems.

[13]  N. Geroliminis,et al.  An analytical approximation for the macropscopic fundamental diagram of urban traffic , 2008 .

[14]  Roger Guimerà,et al.  Extracting the hierarchical organization of complex systems , 2007, Proceedings of the National Academy of Sciences.

[15]  Y. Iwasa,et al.  Pollen coupling of forest trees: forming synchronized and periodic reproduction out of chaos. , 2000, Journal of theoretical biology.

[16]  S. Havlin,et al.  Diffusion and Reactions in Fractals and Disordered Systems , 2000 .

[17]  R D Bretherton,et al.  SCOOT-a Traffic Responsive Method of Coordinating Signals , 1981 .

[18]  Adilson E Motter,et al.  Cascade-based attacks on complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  V. Plerou,et al.  A theory of power-law distributions in financial market fluctuations , 2003, Nature.

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

[21]  Bosiljka Tadic,et al.  Congestion patterns of traffic studied on Nanjing city dual graph , 2009, 2009 16th International Conference on Digital Signal Processing.

[22]  M. Moses,et al.  Cities as Organisms: Allometric Scaling of Urban Road Networks , 2008 .

[23]  HERBERT A. SIMON,et al.  The Architecture of Complexity , 1991 .

[24]  F. Radicchi,et al.  Complex networks renormalization: flows and fixed points. , 2008, Physical review letters.

[25]  Tao Zhou,et al.  Advanced information feedback in intelligent traffic systems. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Jasmine Novak,et al.  Geographic routing in social networks , 2005, Proc. Natl. Acad. Sci. USA.

[27]  Rosemary Braun,et al.  Partition decoupling for multi-gene analysis of gene expression profiling data , 2010, BMC Bioinformatics.

[28]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[29]  R. Boschma,et al.  The Spatial Evolution of Innovation Networks: A Proximity Perspective , 2010 .

[30]  Alessandro Vespignani,et al.  The Role of Geography and Traffic in the Structure of Complex Networks , 2007, Adv. Complex Syst..

[31]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[32]  V. Latora,et al.  Centrality in networks of urban streets. , 2006, Chaos.

[33]  Hu,et al.  1/f noise in a two-lane highway traffic model. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

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

[35]  Tang,et al.  Self-Organized Criticality in Nonconserved Systems. , 1995, Physical review letters.

[36]  Carlos F. Daganzo,et al.  Urban Gridlock: Macroscopic Modeling and Mitigation Approaches , 2007 .

[37]  Dirk Helbing,et al.  The spatial variability of vehicle densities as determinant of urban network capacity , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[38]  A. P. Masucci,et al.  Random planar graphs and the London street network , 2009, 0903.5440.

[39]  Reuven Cohen,et al.  Complex Networks: Structure, Robustness and Function , 2010 .

[40]  Renaud Lambiotte,et al.  Uncovering space-independent communities in spatial networks , 2010, Proceedings of the National Academy of Sciences.

[41]  Athanasios K. Ziliaskopoulos,et al.  Foundations of Dynamic Traffic Assignment: The Past, the Present and the Future , 2001 .

[42]  Roger Guimerà,et al.  Modeling the world-wide airport network , 2004 .

[43]  Choi,et al.  Traffic flow and 1/f fluctuations. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[44]  D. Rockmore,et al.  Topological structures in the equities market network , 2008, Proceedings of the National Academy of Sciences.

[45]  Rui Jiang,et al.  Discontinuous transition from free flow to synchronized flow induced by short-range interaction between vehicles in a three-phase traffic flow model , 2009 .

[46]  Henrik Jeldtoft Jensen,et al.  Diffusive description of lattice gas models , 1993 .

[47]  Daniel N. Rockmore,et al.  Partition Decomposition for Roll Call Data , 2011, ArXiv.

[48]  N. F. Stewart,et al.  The Gravity Model in Transportation Analysis - Theory and Extensions , 1990 .

[49]  H Lieu,et al.  TRAFFIC-FLOW THEORY , 1999 .

[50]  Danny Weyns,et al.  Anticipatory Vehicle Routing using Delegate Multi-Agent Systems , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[51]  Haris N. Koutsopoulos,et al.  Simulation Laboratory for Evaluating Dynamic Traffic Management Systems , 1997 .

[52]  Paczuski,et al.  Emergent traffic jams. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[53]  D. Helbing,et al.  Growth, innovation, scaling, and the pace of life in cities , 2007, Proceedings of the National Academy of Sciences.

[54]  Hai Yang,et al.  Multiple equilibrium behaviors and advanced traveler information systems with endogenous market penetration , 1998 .

[55]  Marina Meila,et al.  Comparing Clusterings by the Variation of Information , 2003, COLT.

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

[57]  S. Havlin,et al.  How to calculate the fractal dimension of a complex network: the box covering algorithm , 2007, cond-mat/0701216.

[58]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[59]  T. Musha,et al.  The 1/f Fluctuation of a Traffic Current on an Expressway , 1976 .

[60]  Dušan Teodorović,et al.  Swarm intelligence systems for transportation engineering: Principles and applications , 2008 .

[61]  S N Dorogovtsev.  Renormalization group for evolving networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[62]  H. F. Smith.  An empirical law describing heterogeneity in the yields of agricultural crops , 1938, The Journal of Agricultural Science.

[63]  Dirk Helbing,et al.  Coherent moving states in highway traffic , 1998, Nature.

[64]  Alan C. Evans,et al.  Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. , 2007, Cerebral cortex.

[65]  L. R. Taylor,et al.  Aggregation, Variance and the Mean , 1961, Nature.

[66]  V Latora,et al.  Analysis of self-organized criticality in the Olami-Feder-Christensen model and in real earthquakes. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[67]  H. Greenberg.  An Analysis of Traffic Flow , 1959 .

[68]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[69]  Alessandro Vespignani,et al.  The effects of spatial constraints on the evolution of weighted complex networks , 2005, physics/0504029.

[70]  Moshe Ben-Akiva,et al.  Adaptive route choices in risky traffic networks: A prospect theory approach , 2010 .

[71]  Kerner,et al.  Experimental features and characteristics of traffic jams. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[72]  H. Stanley,et al.  Networks formed from interdependent networks , 2011, Nature Physics.

[73]  J. Kertész,et al.  Fluctuation scaling in complex systems: Taylor's law and beyond , 2007, 0708.2053.

[74]  M. Ben-Akiva,et al.  Cognitive cost in route choice with real-time information: An exploratory analysis , 2011 .

[75]  H. Pietrantonio.  URBAN TRAVEL DEMAND MODELING: FROM INDIVIDUAL CHOICES TO GENERAL EQUILIBRIUM , 1997 .

[76]  D. Helbing.  Derivation of a fundamental diagram for urban traffic flow , 2008, 0807.1843.