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Traffic Breakdown Probability Estimation for Mixed Flow of Autonomous Vehicles and Human Driven Vehicles.

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Area of Science:

  • Traffic engineering
  • Transportation systems analysis
  • Queueing theory

Background:

  • Automated vehicles (AVs) promise enhanced traffic efficiency.
  • Estimating traffic breakdown probability in mixed human-driven and AV traffic, particularly at ramps, is a significant challenge.

Purpose of the Study:

  • To develop a stochastic temporal queueing model for mixed traffic flow at ramping bottlenecks.
  • To analyze the impact of AV penetration rates and driving strategies on traffic breakdown probability.

Main Methods:

  • A modified Newell's car-following model incorporating two vehicle velocities and FIFO queueing.
  • Simulation of jam queue dynamics, focusing on velocity drops rather than jam duration.
  • Monte Carlo simulations to validate the model against empirical data.

Main Results:

  • The model accurately predicts breakdown probability for pure human-driven traffic.
  • AV driving strategies significantly influence breakdown probabilities.
  • A penetration rate of over 20% AVs, with appropriate strategies, shifts breakdown probability curves rightward, indicating improved traffic flow.

Conclusions:

  • The proposed stochastic temporal queueing model effectively depicts mixed traffic dynamics at ramps.
  • AVs have the potential to substantially improve traffic efficiency and reduce breakdown probability.
  • Optimizing AV driving strategies is crucial for realizing their full traffic management benefits.