The Double Lanes Cell Transmission Model of Mixed Traffic Flow in Urban Intelligent Network

Tian, Wenjing; Ma, Jien; Lin, Qiu; Wang, Xiang; Lin, Zhenzhi; Luo, Chao; Li, Yao; Fang, Youtong

doi:10.3390/en16073108

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…The researchers note that growing automobilization leads to dense traffic flows, increased complexity or traffic organization and a more negative consequences in this connection (such as a high noise level, increased cost of transportation, more accidents, etc.) [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Research targets on the map of Moscow (Yandex Maps: https://yandex.ru/maps/-/CCUWnOU~tC)To create a concept of sustainable development of the district, the following methods were used:1. Divergence, transformation and convergence: The authors used all the above methods in combination to determine relevant boundaries of the research target and its key objects, specify limitations related with characteristics of the problem domain and form details of research solutions meeting the query criteria[3,4]. 2.…”

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confidence: 99%

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Concept and Imitation Modeling of Sustained Transport Model in Moscow Area

et al. 2023

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The article presents the concept of sustainable development of a street road network segment connecting the Koptevo district and the Timiryazevskiy district in the Northern Administrative District of Moscow. When designing the concept of changes, the authors used common scientific methods in their research in order to determine problems and related domain objects. It was found that the sustainable development of this area requires modernization of the district’s transport system. All traffic flows, their objects, relations and external characteristics affecting the nature of traffic were identified for this road segment. This resulted in a formal model of traffic flow. By analyzing the research findings, the authors developed a concept of road infrastructure changes to improve the time, environmental and safe efficiency of traffic flows. To substantiate the efficiency, a formal traffic model was developed along with its respective parametric model being a basis for the imitation model.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Concept and Imitation Modeling of Sustained Transport Model in Moscow Area

et al. 2023

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Analysis of mixed traffic flow with different lane management strategy for connected automated vehicles: A fundamental diagram method

Wang,

Xu,

Yao

et al. 2024

Expert Systems with Applications

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Compressible Non-Newtonian Fluid Based Road Traffic Flow Equation Solved by Physical-Informed Rational Neural Network

Yang,

Li,

Nai

et al. 2024

IEEE Access

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The study of road traffic flow theory utilizes physics and applied mathematics to analyze relevant parameters and their relationships quanlitatively and quantitatively, in order to explore their dynamic changes. The fluid dynamics model used for traffic flow analysis is highly favored by scholars due to its solid mathematical foundation and good simulation results. However, existing models have two main shortcomings: firstly, existing research is mostly limited to non-viscoelastic fluid equation or incompressible non-Newtonian fluid equation, making it difficult to accurately describe the viscosity state and micro cluster properties of the actual traffic flow; secondly, the existing non-Newtonian fluid partial differential equations (PDEs) rely heavily on the finite element method (FEM) for solving, requiring higher computational cost, larger storage space, and more constraint conditions. Thus, in this paper, a traffic flow equation based on compressible non-Newtonian fluid has been constructed, and it has been solved by using physical-informed rational neural network (PIRNN) and noise heavy-ball acceleration gradient descent (NHAGD) to ensure learning and training speed and accuracy. Numerical results indicate that the proposed method can truly reflect the gradual change process in the viscosity of traffic flow, and has better solving performance than traditional FEM and physical-informed neural network (PINN) with activation functions; under the same conditions, the prediction error of the proposed method is also smaller than that of traditional traffic flow models.

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The Double Lanes Cell Transmission Model of Mixed Traffic Flow in Urban Intelligent Network

Cited by 4 publications

References 32 publications

Concept and Imitation Modeling of Sustained Transport Model in Moscow Area

Concept and Imitation Modeling of Sustained Transport Model in Moscow Area

Analysis of mixed traffic flow with different lane management strategy for connected automated vehicles: A fundamental diagram method

Compressible Non-Newtonian Fluid Based Road Traffic Flow Equation Solved by Physical-Informed Rational Neural Network

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