Micro- and macro-simulation of freeway traffic

Helbing, Dirk; Hennecke, Ansgar; Shvetsov, V. I.; Treiber, Martin

doi:10.1016/s0895-7177(02)80019-x

Cited by 269 publications

(130 citation statements)

References 49 publications

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“…In particular the diagram of the congested patterns at on-ramps is qualitatively different in three-phase traffic theory [8,45,24] from the diagram obtained in the fundamental diagram approach [1,10,12,13,14,15,17]. Specifically, at a high flow rate to the on-ramp, instead of HCT without moving jams, we find that moving jams always spontaneously emerge in synchronized flow of low vehicle speed and high density, whereas synchronized flow of higher vehicle speed can exist for a long time without an occurrence of moving jams [8,24,45].…”

Section: The Three-phase Traffic Theorymentioning

confidence: 91%

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Cellular automata approach to three-phase traffic theory

Kerner

Klenov

Wolf³

2002

J. Phys. A: Math. Gen.

416

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: The Three-phase Traffic Theorymentioning

confidence: 91%

“…Different explanations of these empirical findings have been proposed by various groups in the last years, but so far they remain controversial (see e.g., [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26] and the excellent review by Helbing [1]). …”

Section: Introductionmentioning

confidence: 99%

Cellular automata approach to three-phase traffic theory

Kerner

Klenov

Wolf³

2002

J. Phys. A: Math. Gen.

416

214

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“…where τ is a relaxation constant and P (x, t) a pressure term, which gives rise to a range of different models, [13].…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThis paper is concerned with the problem of macroscopic road traffic flow model calibration and verification. Thoroughly validated models are necessary for both control system design and scenario evaluation purposes. Here, the second order traffic flow model METANET was calibrated and verified using real data.A powerful optimisation problem formulation is proposed for identifying a set of model parameters that makes the model fit to measurements. For the macroscopic traffic flow model validation problem, this set of parameters characterise the aggregate traffic flow features over a road network. In traffic engineering, one of the most important relationships whose parameters need to be determined is the fundamental diagram of traffic, which models the non-linear relationship between vehicular flow and density. Typically, a real network does not exhibit the same traffic flow aggregate behaviour everywhere and different fundamental diagrams are used for covering different network areas. As a result, one of the initial steps of the validation process rests on expert engineering opinion assigning the spatial extension of fundamental diagrams. The proposed optimisation problem formulation allows for automatically determining the number of different fundamental diagrams to be used and their corresponding spatial extension over the road network, simplifying this initial step. Although the optimisation problem suffers from local minima, good solutions which generalise well were obtained.The design of the system used is highly generic and allows for a number of evolutionary and swarm intelligence algorithms to be used. Two UK sites have been used for testing it. Calibration and verification results are Preprint submitted to Applied Soft Computing September 18, 2015 discussed in detail. The resulting models are able to capture the dynamics of traffic flow and replicate shockwave propagation. A total of ten different algorithms were considered and compared with respect to their ability to converge to a solution, which remains valid for different sets of data. Particle Swarm Optimisation (PSO) algorithms have proven to be particularly effective and provide the best results both in terms of speed of convergence and solution generalisation. An interesting result reported is that more recently proposed PSO algorithms were outperformed by older variants, both in terms of speed of convergence and model error minimisation.

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“…The numerical tests have been made using a micro-simulator designed adhoc and based on car following models (see G a b a r d [7] and H e l b i n g et al [9]). The idea is to use different models for control derivation and for control testing.…”

Section: Numerical Experimentsmentioning

confidence: 99%