A high performance approach with MATSim for traffic road simulation

Moukir, Sara; Emad, Nahid; Baudelocq, Stephane

doi:10.1109/iiaiaai55812.2022.00140

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…The theoretical research of road traffic flow is to use physics and applied mathematics to conduct qualitative and quantitative research on its parameters and their interrelationships in order to seek its dynamic change law (theoretical validity). Among many traffic flow theories, fluid dynamics models have attracted the attention of scholars in the field of transportation engineering due to their solid mathematical foundation and good simulation results [3][4][5]. Therefore, based on fluid dynamics models, it is of great significance to get higher accuracy in interpreting the changes in actual road traffic flow, assisting urban traffic managers in understanding the changes in road flow, making more reasonable control decisions, and improving the mobility of urban road traffic systems.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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Changes in the equilibrium of the urban transport system of a large city following an urban flood

Borowska-Stefańska,

Bartnik,

Dulebenets

et al. 2025

Reliability Engineering & System Safety

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Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles

Łach,

Svyetlichnyy

2024

Applied Sciences

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Autonomous vehicles (AVs) have the potential to revolutionize transportation by offering safer, more efficient, and convenient mobility solutions. As AV technology advances, there is a growing need to understand and model traffic dynamics in environments where AVs interact with human-driven vehicles. This review provides a comprehensive overview of the modeling techniques used to simulate and analyze autonomous vehicle traffic. It covers the fundamental principles of AVs, key factors influencing traffic dynamics, various modeling approaches, their applications, challenges, and future directions in AV traffic modeling.

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A high performance approach with MATSim for traffic road simulation

Cited by 3 publications

References 10 publications

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

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

Changes in the equilibrium of the urban transport system of a large city following an urban flood

Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles

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