A macroscopic traffic model based on relaxation time

Khan, Zahoor; Gulliver, T. Aaron; Imran, Waheed; Khattak, Khurram S.; Altamimi, Ahmed B.; Qazi, Azhar

doi:10.1016/j.aej.2021.06.042

Cited by 14 publications

(7 citation statements)

References 25 publications

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“…The simulation parameters are given in Table 5. The maximum density is 𝝆 𝒎 = 𝟏 which means pedestrians cover 100% of the area [78]. The target is Greenshields equilibrium velocity distribution to align to forward conditions.…”

Section: Performance Resultsmentioning

confidence: 99%

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Pedestrian Traffic Characterization Based on Pedestrian Response

et al. 2022

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Pedestrian behavior is studied during normal and emergency evacuation of two groups of students. One group consists of grade nine high school students while the other group includes diploma (college) students. The evacuation time, number of steps, step frequency, and velocity are observed. It is found that the number of steps, step frequency, and velocity are larger during an emergency evacuation. These results are used to develop a macroscopic pedestrian response model. This model is compared with the Lighthill, Whitham, and Richards (LWR) model for pedestrian traffic using the First Order Centered (FORCE) scheme. Pedestrian parameters from the experiments are used for model evaluation. The direction of pedestrian movement is changed multiple times to observe pedestrian alignment behavior. It has been found that the proposed model performs more realistically than the LWR model.

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Section: Performance Resultsmentioning

confidence: 99%

“…Macroscopic, mesoscopic, and microscopic traffic flow models have been developed [34], [35]. Macroscopic models are employed when aggregate behavior based on speed, density, and flow are considered and effects such as congestion, delay, and queue formation are important [35]. Thus, these models are more appropriate for crowd dynamics.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Traffic Characterization Based on Pedestrian Response

et al. 2022

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“…This traffic flow characterization is achieved by integrating sensors in or above roads for data collection and processing. These parameters are then employed for validation and calibration of traffic flow mathematical models and traffic simulation software such as VISSIM, Paramics, Aimsun to name a few [5,6,7,8,9,10]. In this context, enormous effort has gone into proposing different solutions for traffic flow characterization in existing literature.…”

Section: Introductionmentioning

confidence: 99%

Sensing technologies for traffic flow characterization: From heterogeneous traffic perspective

Khattak¹,

Minallah²,

Khan³

et al. 2022

J Appl Eng Science

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Importance of detailed traffic flow characterization is immense for achieving intelligent transportation system. As such, great efforts in existing literature have gone into proposing different solutions for traffic flow characterization. Among these, first generation intrusive sensors such as pneumatic tube, inductive loop, piezoelectric and magnetic sensors were both labor intensive and expensive to install and maintain. These sensors were able to provide only vehicle count and classification under homogenous traffic conditions. Second generation non-intrusive sensors based solutions, though a marked improvement over intrusive sensors have the capability to only measure vehicle count, speed and classifications. Furthermore, both intrusive and non-intrusive sensors based solutions have limitations when employed under congested and heterogeneous traffic conditions. To overcome these limitations, a compute vision methodology has been employed for traffic characterization under heterogeneous traffic conditions. The proposed solution was field tested on a complex road configuration, comprising of a two-way multilane road with three U-turns. Unlike both intrusive and non-intrusive sensors, the proposed solution can detect pedestrians, two/three wheelers and animal/human driven carts. Furthermore, detailed flow parameters such as vehicle count, speed, spatial/temporal densities, trajectories and heat maps were measured.

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“…Because of this critique by [9], Aw and Rascle postulated a new pressure term of the form P (k) = k γ with γ > 0 [3,32]. However, the AR model was found to be ill-posed for a virtual road [27]. Besides, [23] in their bid to correct the deficiency of negative wave speed replaced the density gradient term with a velocity gradient component, hence defined anticipation as…”

Section: Introductionmentioning

confidence: 99%

“…As stated in Table 1, earlier formulations are devoid of either the diffusion or viscous term. As such, most recent model presentations and analyses also fellow suite [2,4,7,17,27,41] with few considering the effect of stochasticity [4,42]. Concerning traffic stability, the steady-state condition of a viscous second-order macroscopic traffic flow model was performed by [1].…”

Section: Introductionmentioning

confidence: 99%

Macroscopic Analysis Of The Viscous-Diffusive Traffic Flow Model

Fosu¹,

Adu-Sackey²,

Ackora-Prah³

2022

Math Appl Sci Eng

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Second-order macroscopic traffic models are characterized by a continuity equation and an acceleration equation. Convection, anticipation, relaxation, diffusion, and viscosity are the predominant features of the different classes of the acceleration equation. As a unique approach, this paper presents a new macro-model that accounts for all these dynamic speed quantities. This is done to determine the collective role of these traffic quantities in macroscopic modeling. The proposed model is solved numerically to explain some phenomena of a multilane traffic flow. It also includes a linear stability analysis. Furthermore, the evolution of speed and density wave profiles are presented under the perturbation of some parameters.

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A macroscopic traffic model based on relaxation time

Cited by 14 publications

References 25 publications

Pedestrian Traffic Characterization Based on Pedestrian Response

Pedestrian Traffic Characterization Based on Pedestrian Response

Sensing technologies for traffic flow characterization: From heterogeneous traffic perspective

Macroscopic Analysis Of The Viscous-Diffusive Traffic Flow Model

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