Fundamentals of traffic flow

Helbing, Dirk

doi:10.1103/physreve.55.3735

Cited by 96 publications

(55 citation statements)

References 13 publications

(23 reference statements)

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“…The author also shows that at low densities, velocities of cars and trucks are unequal and that they are equal at high densities. Requirement 5 is also in line with observed low velocity variance at high densities (21). Requirements 6 and 7 may seem trivial.…”

Section: Introductionsupporting

confidence: 64%

New Generic Multiclass Kinematic Wave Traffic Flow Model

Wageningen-Kessels

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Hoogendoorn

et al. 2014

Transportation Research Record

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We propose and analyze a generic multi-class kinematic wave traffic flow model: Fastlane. The model takes into account heterogeneity among driver-vehicle units with respect to speed and space occupancy: long vehicles with large headways (e.g. trucks) take more space than short vehicles with short headways (e.g. passenger cars). Moreover, and this is what makes the model unique, this effect is larger when the traffic volume is higher. This state dependent space occupancy is reflected in dynamic passenger car equivalent values. The resulting model is shown to satisfy important requirements such as providing a unique solution and being anisotropic. Simulations are applied to compare Fastlane to other multiclass models. Furthermore, we show that the characteristic velocity depends on the truck share, which is one of the main consequences of our modeling approach. FLM van Wageningen-Kessels et al 3 INTRODUCTIONTraffic flow theory aims to describe human driving behaviour on road networks, including consistent explanations of observed phenomena such as stop and go traffic, capacity drop and traffic hysteresis. Traffic flow models are traditionally classified into microscopic models, which describe the behavior of individual vehicles, mesoscopic models, which describe traffic on the basis of probability distributions of 'packets' of vehicles, and macroscopic models, which describe traffic as a continuum flow (1, 2, 3). Our focus is on macroscopic traffic flow models, which are widely used to describe and predict traffic flows in larger networks, both in the context of traffic and transportation planning, as well as in management of traffic operations.Macroscopic models describe aggregate driving behavior and typically include an average (equilibrium) relation between traffic density ρ (number of vehicles per unit length) and flow q (number of vehicles per unit time). In kinematic wave (KW) models, traffic is assumed to always be in a state described by this fundamental relation. However, observed density-flow plots usually show wide scatter. One reason for this scatter is that not all the data represent steady-state conditions. Higher-order models explain and reproduce (at least partly) this scatter by assuming accordingly that the traffic state tends towards the fundamental relation but is usually not on it, due to for example anticipation and relaxation effects.A more complete explanation for the scatter in density-flow plots is that it is also related to heterogeneity among drivers and vehicles. Ossen and Hoogendoorn (4) discuss heterogeneity in relation to microscopic models and distinguish between intra-and interdriver heterogeneity. The first relates to changes in behavior of a single driver over time. Additionally, inter-driver heterogeneity relates to structural differences in behavior and/or capabilities between vehicles and drivers. For example, trucks are usually longer and slower than cars, and have different drive characteristics (e.g. maximum acceleration and deceleration capabilities). As a result...

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Section: Introductionsupporting

confidence: 64%

New Generic Multiclass Kinematic Wave Traffic Flow Model

Wageningen-Kessels

Lint

Hoogendoorn

et al. 2014

Transportation Research Record

View full text Add to dashboard Cite

show abstract

“…The results discussed in the preceeding subsections are based on time averaged local measurements. But it is also very useful to analyze the single-vehicle data directly [46][47][48]41]. The single-vehicle data allows calculation of the timeheadway distributions [41].…”

Section: Time-and Distance-headwaysmentioning

confidence: 99%

“…The main advantage of the forms (45) and (46) of the OV function is that exact analytical calculations, e.g. in the jammed region, are possible [92].…”

Section: Optimal Velocity Modelsmentioning

confidence: 99%

Statistical physics of vehicular traffic and some related systems

2000

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In the so-called "microscopic" models of vehicular traffic, attention is paid explicitly to each individual vehicle each of which is represented by a "particle"; the nature of the "interactions" among these particles is determined by the way the vehicles influence each others' movement. Therefore, vehicular traffic, modeled as a system of interacting "particles" driven far from equilibrium, offers the possibility to study various fundamental aspects of truly nonequilibrium systems which are of current interest in statistical physics. Analytical as well as numerical techniques of statistical physics are being used to study these models to understand rich variety of physical phenomena exhibited by vehicular traffic. Some of these phenomena, observed in vehicular traffic under different circumstances, include transitions from one dynamical phase to another, criticality and self-organized criticality, metastability and hysteresis, phase-segregation, etc. In this critical review, written from the perspective of statistical physics, we explain the guiding principles behind all the main theoretical approaches. But we present detailed discussions on the results obtained mainly from the so-called "particle-hopping" models, particularly emphasizing those which have been formulated in recent years using the language of cellular automata.

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“…These are situations where large losses might be expected. The flow of granular particles as compared with fluids gave a fruitful comparison, which was borrowed from a theory of many-particle physics [13,14,15]. Helbing's group [16,17] presented a new escape model, which is applicable also in 'panic' situations.…”

Section: Selection Of Modelling Principlesmentioning

confidence: 99%

A Proposal For The Goals And New Techniques Of Modelling Pedestrian Evacuation In Fires

Korhonen¹,

Hostikka²,

Keski-Rahkonen³

2005

Fire Safety Science - Proceedings of the Eigth International Sy

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In this article, we propose the goals for evacuation simulations in the context of the fire safety engineering. It is proposed that the safety of a building design should be measured using F-N plots that are based on the fire statistics. A new evacuation code is developed that allows the modelling of 'panic' situations and interaction between evacuation simulation and the state-of-the-art fire simulation. The major features of the new code are described and first preliminary results are shown. The method presented was found to run satisfactorily, and fast enough for practical purposes. When the results were compared against the results obtained using Simulex and buildingExodus codes, a good agreement was found in two of the three cases but for a case with congested corridor considerable differences occurred.

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Fundamentals of traffic flow

Cited by 96 publications

References 13 publications

New Generic Multiclass Kinematic Wave Traffic Flow Model

New Generic Multiclass Kinematic Wave Traffic Flow Model

Statistical physics of vehicular traffic and some related systems

A Proposal For The Goals And New Techniques Of Modelling Pedestrian Evacuation In Fires

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