Dynamic Traffic Flow–Dispersion Model Based on TTI of Floating Cars

Zhang, Peng; Zhang, Ying Ying; Lei, Fangshu; Zhu, Guangyu; Guo, Jingkun; Quan, Yongshen

doi:10.1061/jtepbs.0000001

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…For the study of traffic flow changes in different scenarios, Zhang et al (2017) have used the travel time index (TTI) probability distribution model to give a universally description of the traffic flow dispersion phenomenon under different road lengths and average speed conditions [6]; Wang et al (2019) have considered a dynamic traffic flow model with intersections as constraints and defined an optimization objective function based on parameters such as intersection throughput and driver waiting time, in order to establish a real-time adaptive traffic control system [7]…”

Section: Literature Review a Emerging Theory Of Traffic Flowmentioning

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: Literature Review a Emerging Theory Of Traffic Flowmentioning

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

View full text Add to dashboard Cite

show abstract

“…According to the concept of minimum potential energy, a system is in stable equilibrium when its potential energy is low. When random disturbance occurs in traffic system, the traffic state changes [23] , and if the delay can be read as a change in traffic state, there is potential energy parameter that returns to the intended traffic state, and potential energy is created. This potential energy is referred to as traffic potential energy in this research.…”

Section: Energy Conversionmentioning

confidence: 99%

Model for Calculating Traffic Mass and Deceleration Delays Based on Traffic Field Theory

Zeng

Can-qi

2023

Preprint

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This essay identifies two typical bottlenecks that occur when a vehicle cannot change lanes: car following and car stopping. The ideas of traffic field and traffic mass are presented in this work. When there are other vehicles in front of the target vehicle within a particular distance, a force is created that affects the target vehicle's driving speed. The characteristics of the driver and the vehicle collectively determine the traffic mass; the driving speed of the vehicle and external variables have no bearing on this. From physical level, this study examines the vehicle's bottleneck when following a car, identifies the outside factors that have an impact on how it drives, takes into account that the vehicle will transform kinetic energy into potential energy during deceleration, and builds a calculation model for traffic mass. The energy-time conversion coefficient is created from an economic standpoint utilizing the social average wage level and the average cost of motor fuel. Vissim simulation program measures the vehicle's deceleration distance and delay under the Wiedemann car following model. The difference between the measured value of deceleration delay acquired by simulation and the theoretical value calculated by the model is compared using the conversion calculation model of traffic mass and deceleration delay. The experimental data demonstrate that the model is reliable since the error rate between the theoretical calculation value of the deceleration delay obtained by the model and the measured value of simulation results is less than 10%. The article's conclusion is that the traffic field has an impact on moving cars on the road and that physical and socioeconomic factors should be taken into account while studying vehicle following behavior. The deceleration delay value of vehicle's driving and traffic mass have a socioeconomic relationship that can be utilized to calculate the energy-time conversion coefficient when dealing with the bottleneck of cars stopping and starting.

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Dynamic Traffic Flow–Dispersion Model Based on TTI of Floating Cars

Cited by 2 publications

References 26 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

Model for Calculating Traffic Mass and Deceleration Delays Based on Traffic Field Theory

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