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A mixed-flow model for heterogeneous vehicles enforcing a movement control protocol utilizing a vehicular size-based

Md Anowar Hossain1, Jun Tanimoto1,2

  • 1Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka, 816-8580, Japan.

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This study introduces a new traffic model accounting for diverse vehicle sizes, improving traffic flow predictions. The model effectively neutralizes flow fields and analyzes density waves, validated by simulations.

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Area of Science:

  • Traffic flow dynamics
  • Continuum mechanics
  • Nonlinear dynamics

Background:

  • Heterogeneous vehicle sizes significantly impact traffic flow patterns.
  • Existing traffic models often oversimplify vehicle size effects.
  • Understanding traffic flow stability is crucial for efficient transportation systems.

Purpose of the Study:

  • To develop a continuum traffic model incorporating heterogeneous vehicle sizes.
  • To introduce a novel equilibrium velocity function dependent on traffic density.
  • To analyze the stability and density wave behavior of the proposed traffic model.

Main Methods:

  • Development of a new equilibrium velocity function.
  • Quantitative comparison with Optimal Velocity (OV) and Full Velocity Difference (FVD) models.
  • Neutral stability tests and nonlinear analysis to deduce the Korteweg-de Vries-Burgers (KdVB) equation.
  • Numerical simulations to validate analytical findings.

Main Results:

  • The new model quantitatively compares OV and FVD models, highlighting the impact of vehicle size.
  • Neutral stability tests confirm the model's capability to neutralize flow fields.
  • Nonlinear analysis successfully derived the KdVB equation, characterizing density wave behavior.
  • Numerical simulations closely matched analytical results, validating the model's predictive power.

Conclusions:

  • The proposed traffic model effectively captures the influence of heterogeneous vehicle sizes on traffic flow.
  • The model provides a robust framework for analyzing traffic flow stability and wave phenomena.
  • The findings offer valuable insights for traffic management and the design of intelligent transportation systems.