An Anisotropic Traffic Model Based on Driver Interaction

Khan, Zahoor; Imran, Waheed; Gulliver, T. Aaron; Khattak, Khurram S.; Wadud, Zahid; Khan, Akhtar Nawaz

doi:10.1109/access.2020.2985668

Cited by 22 publications

(11 citation statements)

References 30 publications

(47 reference statements)

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“…However, these terms can create negative velocities at discontinuities. The Del Castello et al model may also produce unrealistic results at discontinuities [11]. This is because the anticipation and reaction time in this model are too large.…”

Section: Introductionmentioning

confidence: 93%

A Macroscopic Traffic Model Based on the Safe Velocity at Transitions

2021

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The increasing volume of vehicles on the road has had a significant impact on traffic flow. Congestion in urban areas is now a major concern. To mitigate congestion, an accurate model is required which is based on realistic traffic dynamics. A new traffic model is proposed based on the conservation law of vehicles which considers traffic dynamics at transitions. Traffic alignment to forward conditions is affected by the time and distance between vehicles. Thus, the well-known Lighthill, Whitham, and Richards (LWR) model is modified to account for traffic behavior during alignment. A model for inhomogeneous traffic flow during transitions is proposed which can be used to characterize traffic evolution. The performance of the proposed model is compared with the LWR model using the Greenshields and Underwood target velocity distributions. These models are evaluated using the Godunov technique and numerical stability is guaranteed by considering the Courant, Friedrich, and Lewy (CFL) condition. The results obtained show that the proposed model characterizes the flow more realistically, and thus can provide better insight into traffic behavior for use in controlling congestion and pollution levels, and improving public safety. Doi: 10.28991/cej-2021-03091710 Full Text: PDF

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Section: Introductionmentioning

confidence: 93%

A Macroscopic Traffic Model Based on the Safe Velocity at Transitions

2021

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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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“…The traffic flow parameters obtained can be employed for validation and calibration of mathematical traffic flow models and traffic simulation software for better planning, design, and management of road networks [5]- [10].…”

Section: Related Workmentioning

confidence: 99%

“…Real-time traffic flow data is crucial for planning, designing, and efficient management of transportation networks. Further, it can be employed to validate and calibrate mathematical traffic flow models and traffic simulation tools such as Vissim, Paramics, and Corsim [5]- [10]. Typically, traffic characterization is done manually.…”

Section: Introductionmentioning

confidence: 99%

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Khan¹,

Khattak²,

Khan³

et al. 2021

ICST Transactions on Scalable Information Systems

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Real-world traffic flow parameters are fundamental for devising smart mobility solutions. Though numerous solutions (intrusive and non-intrusive sensors) have been proposed, however, these have serious limitations under heterogeneous and congested traffic conditions. To overcome these limitations, a low-cost real-time Internet-of-Video-Things solution has been proposed. The sensor node (fabricated using Raspberry Pi 3B, Pi cameral and power bank) has the capability to stream 2 Mbps MJPEG video of 640x480 resolution and 20 frames per second (fps). The Camlytics traffic analysis software installed on a Dell desktop is employed for traffic flow characterization. The proposed solution was field-tested with vehicle detection rate of 85.3%. The novelty of the proposed system is that in addition to vehicle count, it has the capability to measure speed, density, time headway, time-space diagram and trajectories. Obtained results can be employed for road network planning, designing and management.

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An Anisotropic Traffic Model Based on Driver Interaction

Cited by 22 publications

References 30 publications

A Macroscopic Traffic Model Based on the Safe Velocity at Transitions

A Macroscopic Traffic Model Based on the Safe Velocity at Transitions

Macroscopic Traffic Flow Characterization for Stimuli Based on Driver Reaction

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

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