Traffic and related self-driven many-particle systems

By Dirk Helbing. 2000

3,130 citations recorded by the source corpus.

Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by “phantom traffic jams” even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems “freeze by heating”? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.

Subfields: Control and Systems Engineering, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b7e4781cb053d316171641c17cf96262380041ab

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Citations in the corpus

General Lane-Changing Model MOBIL for Car-Following Models (2007, methodology): “In the past, single-lane car-following models have been successfully applied to describe traffic dynamics (1, 2).”
Continuum limit of self-driven particles with orientation interaction (2007, background): “Hydrodynamic limits have been recently investigated in traffic flow modeling [4, 20] as well as in supply chain research [3, 14].”
Systemic Risks in Society and Economics (2010, background): “[90] D. Helbing (2001) Traffic and related self-driven manyparticle systems.”
Cellular automata models of road traffic (2005, background): “And in contrast to strictly gaseous analogies, the particles in these systems are intelligent and able to learn from past experience, thereby opening the door to the incorporation of behavioura l and psychological aspects [4,5,6].”
Percolation transition in dynamical traffic network with evolving critical bottlenecks (2014, background): “most cases possibly because the percolation of traffic network is not purely random as a result of the correlation in the traffic flow (11).”
Human mobility: Models and applications (2017, background): “Indeed, several reviews exist on this topic [215, 216, 217, 218, 219, 220], so instead of providing a detailed retrospect, we provide here an overview of the most recent developments.”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “The dynamics on road networks has been studied for years, wit h many models proposed [297, 298, 299, 300, 301], ranging from macroscopic models based o n the kinetic gas theory or fluid dynamics to microscopic approaches with equations for each c r in the network.”
Robust Consensus Controller Design for Nonlinear Relative Degree Two Multi-Agent Systems With Communication Constraints (2011, background): “... describes the sensitivity of the driver, represents the desired velocity of all cars, e.g., the speed limit, and describes the attitude of the driver to accelerate and decelerate depending on the consistent desired distance , the actual distance between and , and a scalar gains , . This model is”
The spatial variability of vehicle densities as determinant of urban network capacity (2010, neutral): “It is likely that this property does not only follow from the network structure, but is also a consequence of instabilities of traffic flows (for an overview, see Helbing 2001; Helbing & Moussaid 2009; Helbing et al. 2009).”
Vehicular Mobility Simulation for VANETs (2007, background): “In most of these models, only the macro-mobility of nodes is considered, although car-to-car interaction is a fundamental factor to take into account when dealing with vehicular mobility [14].”
Large Scale Dynamics of the Persistent Turning Walker Model of Fish Behavior (2007, background): “More recently, the derivation of macroscopic models from microscopic behavior has been very productive in other context like traffic [4,33] or supply-chains [3].”