A macroscopic traffic model for heterogeneous flow

Imran, Waheed; Khan, Zahoor; Gulliver, T. Aaron; Khattak, Khurram S.; Nasir, Hassan

doi:10.1016/j.cjph.2019.12.005

Cited by 38 publications

(18 citation statements)

References 30 publications

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“…Traffic can be characterized considering driver behavior and traffic flow theory [43,44]. The steps in developing a traffic flow model are shown in Figure 1.…”

Section: Traffic Flow Modelingmentioning

confidence: 99%

Macroscopic Traffic Flow Characterization for Stimuli Based on Driver Reaction

et al. 2021

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The design and management of infrastructure is a significant challenge for traffic engineers and planners. Accurate traffic characterization is necessary for effective infrastructure utilization. Thus, models are required that can characterize a variety of conditions and can be employed for homogeneous, heterogeneous, equilibrium and non-equilibrium traffic. The Lighthill-Whitham-Richards (LWR) model is widely used because of its simplicity. This model characterizes traffic behavior with small changes over a long idealized road and so is inadequate for typical traffic conditions. The extended LWR model considers driver types based on velocity to characterize traffic behavior in non lane discipline traffic but it ignores the stimuli for changes in velocity. In this paper, an improved model is presented which is based on driver reaction to forward traffic stimuli. This reaction occurs over the forward distance headway during which traffic aligns to the current conditions. The performance of the proposed, LWR and extended LWR models is evaluated using the first order upwind scheme (FOUS). The numerical stability of this scheme is guaranteed by employing the Courant, Friedrich and Lewy (CFL) condition. Results are presented which show that the proposed model can characterize both small and large changes in traffic more realistically. Doi: 10.28991/cej-2021-03091632 Full Text: PDF

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“…Traffic can be characterized considering driver behavior and traffic flow theory [43,44]. The steps in developing a traffic flow model are shown in Figure 1.…”

Section: Traffic Flow Modelingmentioning

confidence: 99%

Macroscopic Traffic Flow Characterization for Stimuli Based on Driver Reaction

et al. 2021

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“…From [41], the distance between vehicles for traffic alignment is h = 1 ρ , so the distance maintained between vehicles at equilibrium is h e = 1 ρ e . Thus, the relaxation term of the proposed model is a function of h. With this model, driver reaction is based on realistic rearward velocity and relaxation.…”

Section: Proposed Traffic Modelmentioning

confidence: 99%

An Anisotropic Traffic Model Based on Driver Interaction

et al. 2020

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The anisotropic Zheng and Jiang models characterize traffic with a constant rearward velocity. They also consider a constant driver sensitivity which can result in unrealistic behavior. In this paper, a new anisotropic model based on a variable rearward velocity is proposed. The transition width, driver reaction, and sensitivity at transitions are used to characterize this velocity. This width impacts traffic alignment with forward conditions and driver reaction can be aggressive, slow or typical. The performance of the proposed, Zheng and Jiang models is evaluated over a 2000 m circular (ring) road with an inactive traffic bottleneck. Results are presented which show that traffic evolution with the proposed model is more realistic than with the Zheng and Jiang models. INDEX TERMS Anisotropic model, driver reaction, rearward velocity, transition dynamics, Zheng model, Jiang model.

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“… Help traffic engineers to identify congested road sections and affiliated environmental costs.  Macroscopic vehicular flow parameters can be employed for validation and calibration of mathematical traffic flow models and traffic simulation software [6,7], [15─18]. Furthermore, a free and open source cloud platform was employed for logging and analysis of data for scalability and reliability.…”

Section: 3counting and Speed Estimationmentioning

confidence: 99%

Cyber physical system for vehicle counting and emission monitoring

Khan¹,

Khattak²,

Khan³

et al. 2020

IJACR

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With 25% share in world"s fossil fuel consumption, the transportation sector is contributing about 29% of worldwide greenhouse gas (GHG) emissions [1]. Furthermore, about 23% of global carbon dioxide (CO2) emissions are attributed to the transportation sector [2]. Other transportation related pollutants include nitric oxides, sulphur dioxide and particulate matter (PM) dust in urban settings. According to WHO report, transportation related ambient air pollution has resulted in 4.2 million premature deaths worldwide in 2016 [3]. About 91% of these deaths are born by low and middle-income countries [3]. With increasing urbanization, traffic related health issues will exasperate further, resulting in increased lung inflammation, cardiovascular, pulmonary and respiratory diseases [3−5].

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A macroscopic traffic model for heterogeneous flow

Cited by 38 publications

References 30 publications

Macroscopic Traffic Flow Characterization for Stimuli Based on Driver Reaction

Macroscopic Traffic Flow Characterization for Stimuli Based on Driver Reaction

An Anisotropic Traffic Model Based on Driver Interaction

Cyber physical system for vehicle counting and emission monitoring

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