Self-organization and a dynamical transition in traffic-flow models

Biham, Ofer; Middleton, A. Alan; Levine, Dov

doi:10.1103/physreva.46.r6124

Cited by 768 publications

(480 citation statements)

References 9 publications

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“…This has triggered speculations about an abrupt transition in traffic flow (Treiterer and Myers 1974, Koshi et al 1983, Acha-Daza and Hall 1993, which were recently backed up by models (Kühne 1984, Kerner and Konhäuser 1994, Bando et al 1994). In addition, the modeling technique of cellular automata (CA), also already introduced in the 1950s (Gerlough 1956, Cremer andLudwig 1986), was used increasingly in the last decade (Biham et al 1992, Nagel and Schreckenberg 1992, Takayasu and Takayasu 1993. Although there is no fundamental reason behind this, CA modeling often included stochastic aspects while the other approaches often did not.…”

Section: Introductionmentioning

confidence: 99%

Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling

Nagel¹,

Wagner

Woesler

2003

Operations Research

286

165

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Certain aspects of traffic flow measurements imply the existence of a phase transition. Models known from chaos and fractals, such as nonlinear analysis of coupled differential equations, cellular automata, or coupled maps, can generate behavior which indeed resembles a phase transition in the flow behavior. Other measurements point out that the same behavior could be generated by geometrical constraints of the scenario. This paper looks at some of the empirical evidence, but mostly focuses on different modeling approaches. The theory of traffic jam dynamics is reviewed in some detail, starting from the well-established theory of kinematic waves and then veering into the area of phase transitions. One aspect of the theory of phase transitions is that, by changing one single parameter, a system can be moved from displaying a phase transition to not displaying a phase transition. This implies that models for traffic can be tuned so that they display a phase transition or not.This paper focuses on microscopic modeling, i.e., coupled differential equations, cellular automata, and coupled maps. The phase transition behavior of these models, as far as it is known, is discussed. Similarly, fluid-dynamical models for the same questions are considered. A large portion of this paper is given to the discussion of extensions and open questions, which makes clear that the question of traffic jam dynamics is, albeit important, only a small part of an interesting and vibrant field. As our outlook shows, the whole field is moving away from a rather static view of traffic toward a dynamic view, which uses simulation as an important tool.

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Section: Introductionmentioning

confidence: 99%

Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling

Nagel¹,

Wagner

Woesler

2003

Operations Research

286

165

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“…In particular, the spontaneous formation of tra c jams provides a rich testbed for studying the emergence of complex activity from seemingly chaotic states 119,121]. Furthermore, the dynamics of tra c ow is particular amenable to the application and testing of many novel numerical methods in a controlled environment 16,30,237]. Many experimental studies have con rmed the usefulness of applying insights gleaned from such w ork to real world tra c scenarios 119,198,197].…”

Section: Tra C Theorymentioning

confidence: 99%

Theory of Collective Intelligence

Wolpert

2004

Collectives and the Design of Complex Systems

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This paper surveys the emerging science of how to design a \COllective INtelligence" (COIN). A COIN is a large multi-agent system where: i) There is little to no centralized, personalized communication and/or control. ii) There is a provided world utility function that rates the possible histories of the full system.In particular, we a r e i n terested in COINs in which e a c h a g e n t runs a reinforcement learning (RL) algorithm. The conventional approach to designing large distributed systems to optimize a world utility does not use agents running RL algorithms. Rather, that approach begins with explicit modeling of the dynamics of the overall system, followed by detailed hand-tuning of the interactions between the components to ensure that they \cooperate" as far as the world utility i s concerned. This approach is labor-intensive, often results in highly nonrobust systems, and usually results in design techniques that have limited applicability. In contrast, we w i s h t o s o l v e the COIN design problem implicitly, via the \adaptive" character of the RL algorithms of each of the agents. This approach i n troduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we d o n o t have phenomena like t h e tragedy of the commons, Braess's paradox, or the liquidity trap? Although still very young, research speci cally concentrating on the COIN design problem has already resulted in successes in arti cial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur's El Farol bar problem. It is expected that as it matures and draws upon other disciplines related to COINs, this research will greatly expand the range of tasks addressable by human engineers. Moreover, in addition to drawing on them, such a fully developed science of COIN design may provide much insight into other already established scienti c elds, such as economics, game theory, and population biology.

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“…An interesting application of cellular automata, studied extensively in both the physics and the engineering communities, is the modeling and analysis of urban vehicular traffic [37,38,39,40]. Gershenson and Rosenblueth [41] apply a twodimensional model based on simple interaction roles.…”

Section: Vehicular Traffic: Application Of Cellular Automata and Agenmentioning

confidence: 99%

A Survey of Models and Design Methods for Self-organizing Networked Systems

Elmenreich

D’Souza

Bettstetter

et al. 2009

Self-Organizing Systems

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Abstract. Self-organization, whereby through purely local interactions, global order and structure emerge, is studied broadly across many fields of science, economics, and engineering. We review several existing methods and modeling techniques used to understand self-organization in a general manner. We then present implementation concepts and case studies for applying these principles for the design and deployment of robust self-organizing networked systems.

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Self-organization and a dynamical transition in traffic-flow models

Cited by 768 publications

References 9 publications

Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling

Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling

Theory of Collective Intelligence

A Survey of Models and Design Methods for Self-organizing Networked Systems

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