Real-Time Lagrangian Traffic State Estimator for Freeways

Yuan, Yufei; Lint, Hans van; Wilson, Roger; Wageningen-Kessels, Femke van; Hoogendoorn, Serge P.

doi:10.1109/tits.2011.2178837

Cited by 177 publications

(88 citation statements)

References 24 publications

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“…Future research also includes testing our hypothesis on the influence of composition on congestion wave velocity. The model has been applied for traffic state estimation (33) and model predictive control (34). The studies show the added value of a multi-class model because it enables multi-class measurements in state estimation.…”

Section: Discussionmentioning

confidence: 99%

“…For discretization, in the original introduction of the Fastlane model (14) the mixed class minimum supply demand method (26,39) is adapted to include multiple classes (41). The mixed class Godunov method in Lagrangian coordinates (33) has been adapted to include multiple vehicle classes and is shown to be more efficient (34). In simulations time is divided into K time steps of size t. Furthermore, vehicles are divided into groups of n vehicles.…”

Section: Discretizationmentioning

confidence: 99%

“…However, t if at all possible, with the proposed model and many vehicle cl original introduction of the Fastlane model (14) the mixed class m (26, 39) is adapted to include multiple classes (41). The mixe grangian coordinates (33) has been adapted to include multiple be more efficient (34). In simulations time is divided into K tim vehicles are divided into groups of n vehicles.…”

Section: Discretizationmentioning

confidence: 99%

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New Generic Multiclass Kinematic Wave Traffic Flow Model

Wageningen-Kessels

Lint

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: Discussionmentioning

confidence: 99%

Section: Discretizationmentioning

confidence: 99%

Section: Discretizationmentioning

confidence: 99%

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New Generic Multiclass Kinematic Wave Traffic Flow Model

Wageningen-Kessels

Lint

Hoogendoorn

et al. 2014

Transportation Research Record

Self Cite

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“…Edge density calculation In fact, there is a linear correlative relationship between the average intensity of background subtract image the occupation ratio [6][7][8] .…”

Section: )mentioning

confidence: 99%

Traffic States Detection based on Block Matching

Jiang¹,

Zhang²,

Wang³

2015

Proceedings of the International Conference on Chemical, Material and Food Engineering

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Abstract-A traffic states detection method is proposed to represent the road traffic conditions accurately and reduce labor intensive monitoring work in this paper. The edge of image, which is named as edge characteristic, is applied to describe the number of vehicles running on the road, and the average density of vehicle edge pixels is used to substitute the road occupation ratio. Meanwhile block matching method is used for estimating the speed of traffic flow. According the extracted feature parameters, a classifier is designed to classify the traffic states in three categories (congested, slow, and smooth). The experimental results showed the proposed algorithm is robust.

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“…Thus, probe vehicles are sampled from all vehicles but have a great advantage over fixed sensor in terms of spatial region of measurement, i.e., we can measure some traffic states on minor roads as long as probe vehicles exist on these roads. Furthermore, since vehicle trajectory data have rich information which include driving modes (e.g., acceleration/deceleration, stop, and free driving) and reflect traffic conditions, probe data is used for estimating traffic state (e.g., Nanthawichit et al [1]; Herrera and Bayen [2]; Yuan et al [3]; Deng et al [4]) and reconstructing vehicle trajectories (e.g., Coifman [5]; Claudel and Bayen [6,7]; Mehran et al [8]) by the fusion of various types of data, such as fixed data and signal timing data.…”

Section: Introductionmentioning

confidence: 99%

Traffic Measurements on Signalized Arterials from Vehicle Trajectories

Wada

Ohata

Kobayashi

et al. 2015

IIS

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Probe data provide rich information of vehicle trajectories which include driving modes (e.g., acceleration/ deceleration) and reflect traffic conditions. However, most of the applications only use probe data to measure travel times. This paper presents two alternative applications of vehicle trajectories on signalized arterials: one is a traffic signal timing estimation and the other is a traffic volume estimation. Both applications are based on a simple methodology that combines vehicle trajectories and traffic engineering concept shock wave. By testing the methodology using real world data, we demonstrate that probe data have more potential for traffic measurements.

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Real-Time Lagrangian Traffic State Estimator for Freeways

Cited by 177 publications

References 24 publications

New Generic Multiclass Kinematic Wave Traffic Flow Model

New Generic Multiclass Kinematic Wave Traffic Flow Model

Traffic States Detection based on Block Matching

Traffic Measurements on Signalized Arterials from Vehicle Trajectories

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