Self-organized network flows

Helbing, Dirk; Siegmeier, Jan; Lämmer, Stefan

doi:10.3934/nhm.2007.2.193

Cited by 46 publications

(29 citation statements)

References 32 publications

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“…Most infrastructure networks are used as means of transporting goods, energy or information [58][59][60]. Information flows through communication networks, epidemics, rumors and opinions through social networks, electrical power through the power grid [61][62][63] and land transportation through road and railway networks [64], etc.…”

Section: State Of the Artmentioning

confidence: 99%

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Havlin

Kenett

Ben‐Jacob

et al. 2012

Eur. Phys. J. Spec. Top.

165

102

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Abstract. Network theory has become one of the most visible theoretical frameworks that can be applied to the description, analysis, understanding, design and repair of multi-level complex systems. Complex networks occur everywhere, in man-made and human social systems, in organic and inorganic matter, from nano to macro scales, and a e-mail: havlins@gmail.com b e-mail: drorkenett@gmail.com c e-mail: eshelbj@gmail.com d e-mail: armin.bunde@physik.uni-giessen.de e e-mail: reuven@macs.biu.ac.il f e-mail: hans@ifb.baug.ethz.ch g e-mail: jan.kantelhardt@physik.uni-halle.de h e-mail: kertesz@phy.bme.hu i e-mail: kirk@cs.huji.ac.il j e-mail: kurths@pik-potsdam.de k e-mail: juval@post.tau.ac.il l e-mail: sorin@vms.huji.ac.il 274The European Physical Journal Special Topics in natural and anthropogenic structures. New applications are developed at an ever-increasing rate and the promise for future growth is high, since increasingly we interact with one another within these vital and complex environments. Despite all the great successes of this field, crucial aspects of multi-level complex systems have been largely ignored. Important challenges of network science are to take into account many of these missing realistic features such as strong coupling between networks (networks are not isolated), the dynamics of networks (networks are not static), interrelationships between structure, dynamics and function of networks, interdependencies in given networks (and other classes of links, including different signs of interactions), and spatial properties (including geographical aspects) of networks. This aim of this paper is to introduce and discuss the challenges that future network science needs to address, and how different disciplines will be accordingly affected.

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Section: State Of the Artmentioning

confidence: 99%

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Havlin

Kenett

Ben‐Jacob

et al. 2012

Eur. Phys. J. Spec. Top.

165

102

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“…As an example of an adaptive traffic signal system, we study a variation of the 'self-organizing' traffic lights (SOTL) model studied in [15]. Such traffic signal systems have been the subject of several recent studies in the statistical mechanics literature [21,22,23,24]. In general, each node in a network has a number of signal phases 3 , which determine which incoming links have right of way at a given instant.…”

Section: Signal Systemsmentioning

confidence: 99%

Traffic disruption and recovery in road networks

Zhang

Gier

Garoni

2014

Physica A: Statistical Mechanics and its Applications

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We study the impact of disruptions on road networks, and the recovery process after the disruption is removed from the system. Such disruptions could be caused by vehicle breakdown or illegal parking. We analyze the transient behavior using domain wall theory, and compare these predictions with simulations of a stochastic cellular automaton model. We find that the domain wall model can reproduce the time evolution of flow and density during the disruption and the recovery processes, for both one-dimensional systems and two-dimensional networks

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“…By this we mean the oscillatory pattern showing up when groups of cars from incoming roads occupy alternately a junction, giving rise to a sequence of density wave packets along the outgoing roads. This usually occurs at jammed crossroads not regulated by any imposed red-green traffic light cycle [11].…”

Section: Introductionmentioning

confidence: 99%

How can macroscopic models reveal self-organization in traffic flow?

Cristiani

Piccoli

Tosin

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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In this paper we propose a new modeling technique for vehicular traffic flow, designed for capturing at a macroscopic level some effects, due to the microscopic granularity of the flow of cars, which would be lost with a purely continuous approach. The starting point is a multiscale method for pedestrian modeling, recently introduced in [5], in which measure-theoretic tools are used to manage the microscopic and the macroscopic scales under a unique framework. In the resulting coupled model the two scales coexist and share information, in the sense that the same system is simultaneously described from both a discrete (microscopic) and a continuous (macroscopic) perspective. This way it is possible to perform numerical simulations in which the single trajectories and the average density of the moving agents affect each other. Such a method is here revisited in order to deal with multi-population traffic flow on networks. For illustrative purposes, we focus on the simple case of the intersection of two roads. By exploiting one of the main features of the multiscale method, namely its dimension-independence, we treat one-dimensional roads and two-dimensional junctions in a natural way, without referring to classical network theory. Furthermore, thanks to the coupling between the microscopic and the macroscopic scales, we model the continuous flow of cars without losing the right amount of granularity, which characterizes the real physical system and triggers self-organization effects, such as, for example, the oscillatory patterns visible at jammed uncontrolled crossroads.

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Self-organized network flows

Cited by 46 publications

References 32 publications

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Traffic disruption and recovery in road networks

How can macroscopic models reveal self-organization in traffic flow?

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