Operational Effects of Acceleration Lane on Main Traffic Flow at Discontinuities

Ngoduy, Dong

doi:10.1080/18128600808685687

Cited by 20 publications

(10 citation statements)

References 27 publications

(45 reference statements)

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“…It produces oscillations owing to a braking probability that can be constant or a function of the speed (Barlovic et al 1998(Barlovic et al , 2002, as do the second-order macroscopic models proposed by Khoshyaran & Lebacque (2007). Gas-kinetic models are inherently stochastic (Helbing & Treiber 1998;Shvetsov & Helbing 1999;Helbing et al 2001;Ngoduy et al 2006;Ngoduy 2008), but have decreased in popularity, probably because of the large number of non-physical parameters needed, and the complexity of their implementation. Del Castillo (2001) and Kim & Zhang (2008) took Newell's conjectures, supposed that the paths between the two congested branches were random and showed how this produces oscillations that grow or dissipate.…”

Section: Introductionmentioning

confidence: 99%

A mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic

Laval

Leclercq

2010

Phil. Trans. R. Soc. A.

231

102

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This paper introduces a parsimonious theory for congested freeway traffic that describes the spontaneous appearance of oscillations and their ensuing transformation into stopand-go waves. Based upon the analysis of detailed vehicle-trajectory data, we conclude that timid and aggressive driver behaviours are the cause for this transformation. We find that stop-and-go waves arise independently of the details of these behaviours. Analytical and simulation results are presented.

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Section: Introductionmentioning

confidence: 99%

A mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic

Laval

Leclercq

2010

Phil. Trans. R. Soc. A.

231

102

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“…The derivation method in this section is a so‐called method of moments. In principle, the method of moments has been applied widely to obtain macroscopic traffic models from gas‐kinetic theory in literature (Treiber et al., ; Helbing et al., ; Hoogendoorn et al., ; Ngoduy et al., ; Ngoduy, ; Ngoduy and Tampere, ). By definition, the macroscopic traffic variables are determined as below: Density

r (x, t

) describing the number of vehicles per unit road length

[x, x + d x]

at time t .…”

Section: Multianticipative Macroscopic Modelmentioning

confidence: 99%

Multianticipative Nonlocal Macroscopic Traffic Model

Ngoduy

Wilson

2013

Computer aided Civil Eng

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Multianticipative driving behavior, where a vehicle reacts to many vehicles in front, has been extensively studied and modeled using a car‐following (i.e., microscopic) approach. A lot of effort has been undertaken to model such multianticipative driving behavior using a macroscopic approach, which is useful for real‐time prediction and control applications due to its fast computational demand. However, these macroscopic models have increasingly failed with an increased number of anticipations. To this end, this article puts forward derivation of an improved macroscopic model for multianticipative driving behavior using a modified gas‐kinetic approach. First, the basic (microscopic) generalized force model, which has been claimed to fit well with real traffic data, is chosen for the derivation. Second, the derivation method relaxes the condition that deceleration happens instantaneously. Theoretical analysis and numerical simulations of the model are carried out to show the improved performance of the derived model over the existing (multianticipative) macroscopic models.

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“…This section mainly focuses on the description of the higher macroscopic models, which are relevant for freeway traffic dynamics in replicating many realistic congested traffic states (Helbing et al, 1999; Ngoduy, 2008b). The additional equation for the speed (or flow) dynamics allows the higher order models to estimate the speed better than the first order model, which assumes a nonrealistic equilibrium density and speed relationship.…”

Section: Dynamic Equations Of Traffic Flow Operationsmentioning

confidence: 99%

Kernel Smoothing Method Applicable to the Dynamic Calibration of Traffic Flow Models

Ngoduy¹

2010

Computer-Aided Civil and Infrastructure Engineering

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Real time traffic flow simulation models are used to provide traffic information for dynamic traffic management systems. Those simulation models are supplied by traffic data in order to estimate and predict traffic conditions in unobserved sections of a traffic network. In general, most of recent real time traffic simulators are based on the macroscopic model because the macroscopic model replicates the average traffic behavior in terms of observable variables such as (time–space) flow and speed at a relatively fast computational time. Like other simulation models, an important aspect of the real time macroscopic simulator is to calibrate the model parameters online. The most conventional way of the online calibration is to add a random walk to the parameters to constitute an augmentation of the traffic variables and the model parameters to be estimated. Actually, this method allows the parameters to vary at every time step and, therefore, describes the adaptation of the model to the prevailing traffic conditions. However, it has been reported that the use of the random walk results in a loss of information and an increase of the covariance of parameters, which consequently leads to posteriors that are far more diffuse than the theoretical posteriors for the true parameters. To this end, this article puts forward a Kernel density estimation technique in the calibration process to handle the covariance issue and to avoid the information loss. The Kernel density estimation technique is embedded in the particle filter algorithm, which is extended to the calibration problems. The proposed framework is investigated using real‐life data collected in a freeway in England.

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Operational Effects of Acceleration Lane on Main Traffic Flow at Discontinuities

Cited by 20 publications

References 27 publications

A mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic

A mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic

Multianticipative Nonlocal Macroscopic Traffic Model

Kernel Smoothing Method Applicable to the Dynamic Calibration of Traffic Flow Models

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