Metastable states in a microscopic model of traffic flow

Krauß, S.; Wagner, Péter; Gawron, C.

doi:10.1103/physreve.55.5597

Cited by 532 publications

(252 citation statements)

References 10 publications

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“…The characteristic parameters of wide moving jam propagation which were found in empirical observations [59,41,4,7] can be reproduced in many traffic flow models in the fundamental diagram approach, where they have first been predicted by Kerner and Konhäuser in 1994 [33] (see also the later papers by Bando, Sugiyama et al [60], by Krauß et al [46], by Barlovic et al [39], and the reviews by Chowdhury et al [2] and by Helbing [1]). As already mentioned, the slow-to-start rules [51,39,22] allow the wide moving jam propagation through different traffic states and bottlenecks keeping the characteristic velocity of the downstream jam front.…”

Section: Wide Moving Jam Propagationmentioning

confidence: 89%

“…v s,n is the save speed which must not be exceeded in order to avoid collisions. In general it depends on the space gap between vehicles, g n = x ℓ,n − x n − d and the speed v ℓ,n [46,40], where the lower index ℓ marks functions (or values) related to the vehicle in front of the one at x n , the "leading vehicle", and d is the vehicle length (assumed to be the same for all vehicles in this paper). For the sake of comparability we neglect the v ℓ,n -dependence and choose the same expression as in the standard NaSch-model [36]:…”

Section: Equations Of Motionmentioning

confidence: 99%

“…Whereas in models belonging to the fundamental diagram approach (e.g., [10,12,16,18,22,23,36,39,46,49,47] and the reviews [2, 1]) a vehicle would close up to the leading one adjusting its speed and gap as required by secure driving, in models with the basic structure (1), (3), a driver within the synchronization distance D n adapts his speed to the one of the vehicle in front without caring, what the precise gap is, as long as it is safe. This explains why there is no unique flow-density relationship for steady states in the present CA models.…”

Section: Steady Statesmentioning

confidence: 99%

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Cellular automata approach to three-phase traffic theory

Kerner

Klenov

Wolf³

2002

J. Phys. A: Math. Gen.

415

214

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Abstract. The cellular automata (CA) approach to traffic modeling is extended to allow for spatially homogeneous steady state solutions that cover a two dimensional region in the flow-density plane. Hence these models fulfill a basic postulate of a three-phase traffic theory proposed by Kerner. This is achieved by a synchronization distance, within which a vehicle always tries to adjust its speed to the one of the vehicle in front. In the CA models presented, the modelling of the free and safe speeds, the slow-to-start rules as well as some contributions to noise are based on the ideas of the Nagel-Schreckenberg type modelling. It is shown that the proposed CA models can be very transparent and still reproduce the two main types of congested patterns (the general pattern and the synchronized flow pattern) as well as their dependence on the flows near an on-ramp, in qualitative agreement with the recently developed continuum version of the three-phase traffic theory [B. S. Kerner and S. L. Klenov. 2002. J. Phys. A: Math. Gen. 35, L31]. These features are qualitatively different than in previously considered CA traffic models. The probability of the breakdown phenomenon (i.e., of the phase transition from free flow to synchronized flow) as function of the flow rate to the on-ramp and of the flow rate on the road upstream of the on-ramp is investigated. The capacity drops at the on-ramp which occur due to the formation of different congested patterns are calculated. Cellular automata2

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Section: Wide Moving Jam Propagationmentioning

confidence: 89%

Section: Equations Of Motionmentioning

confidence: 99%

Section: Steady Statesmentioning

confidence: 99%

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Cellular automata approach to three-phase traffic theory

Kerner

Klenov

Wolf³

2002

J. Phys. A: Math. Gen.

415

214

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“…SUMO applies Krauss's car following model (Krauss et al, 1997) generating vehicle acceleration based on safe speed profiles. The instantaneous vehicle speed for individual vehicle at each time step ∆t is determined by…”

Section: Traffic Simulatormentioning

confidence: 99%

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

Jin

Kosonen

2017

Advances in Engineering Software

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A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation. Advances in Engineering SoftwareAccess to the published version may require subscription. N.B. When citing this work, cite the original published paper. AbstractTraffic flow is considered as a stochastic process in road traffic modeling. Computer simulation is a widely used tool to represent traffic system in engineering applications. The increased traffic congestion in urban areas and their impacts require more efficient controls and management. While the effectiveness of control schemes highly depends on accurate traffic model and appropriate control settings, optimization techniques play a central role for determining the control parameters in traffic planning and management applications. However, there is still a lack of research effort on the scientific computing framework for optimizing traffic control and operations and facilitating real planning and management applications. To this end, the present study proposes a model-based optimization framework to integrate essential components for solving road traffic control problems in general. In particular, the framework is based on traffic simulation models, while the solution needs extensive computation during the engineering optimization process. In this study, an advanced genetic algorithm, extended by an external archive for storing globally elite genes, governs the computing framework, and it shows superior performance than the ordinary genetic algorithm because of the reduced number of fitness function evaluations in engineering applications. To evaluate the optimization algorithm and validate the whole software framework, this paper illustrates a detailed application for optimization of traffic light controls. The study optimizes a simple road network of two intersections in Stockholm to demonstrate the model-based optimization processes as well as to evaluate the presented algorithm and software performance.

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“…To generate the movements for the simulated vehicles, we used the Krauss mobility model (Krauss et al, 1997) available in SUMO with some modifications to allow multi-lane behavior (Krajzewicz et al, 2012). This model is based on collision avoidance among vehicles by adjusting the speed of a vehicle to the speed of its predecessor using the following formula:…”

Section: Simulation Environment and Previous Conceptsmentioning

confidence: 99%

RTAD: A real-time adaptive dissemination system for VANETs

Sanguesa

Fogue

Garrido

et al. 2015

Computer Communications

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Efficient message dissemination is of utmost importance to propel the development of useful services and applications in Vehicular ad hoc Networks (VANETs).In this paper, we propose a novel adaptive system that allows each vehicle to automatically adopt the optimal dissemination scheme in order to fit the warning message delivery policy to each specific situation. Our mechanism uses as input parameters the vehicular density and the topological characteristics of the environment where the vehicles are located, in order to decide which dissemination scheme to use. We compare our proposal with respect to two static dissemination schemes (eMDR and NJL), and three adaptive dissemination systems (UV-CAST, FDPD, and DV-CAST). Simulation results demonstrate that our approach significantly improves upon these solutions, being able to support more efficient warning message dissemination in all situations ranging from low densities with complex maps, to high densities in simple scenarios. In particular, RTAD improves existing approaches in terms of percentage of vehicles informed, while significantly reducing the number of messages sent, thus mitigating broadcast storms.

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Metastable states in a microscopic model of traffic flow

Cited by 532 publications

References 10 publications

Cellular automata approach to three-phase traffic theory

Cellular automata approach to three-phase traffic theory

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

RTAD: A real-time adaptive dissemination system for VANETs

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