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Urban connected vehicle lane planning based on improved Frank Wolfe algorithm.

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Summary

This study introduces an optimized lane planning method for urban connected vehicles, improving traffic flow. The new approach enhances traffic efficiency and safety in networked environments.

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Area of Science:

  • Intelligent Transportation Systems
  • Traffic Engineering
  • Computer Science

Background:

  • Increasing numbers of intelligent connected vehicles (ICVs) necessitate advanced traffic management.
  • Existing traffic flow optimization methods struggle with the complexities of networked urban environments.

Purpose of the Study:

  • To develop an effective lane planning method for urban connected vehicles in a networked environment.
  • To construct a bi-level programming model for ICV lane planning.
  • To enhance traffic flow optimization and road network efficiency.

Main Methods:

  • A bi-level programming model for urban connected vehicle lane planning was constructed.
  • The upper-level model was solved using an improved whale optimization algorithm.
  • The lower-level model was solved using an improved Frank-Wolfe algorithm.

Main Results:

  • The improved whale optimization algorithm demonstrated superior accuracy (95.27%) and recall (92.65%) compared to other algorithms.
  • The improved Frank-Wolfe algorithm achieved convergence within approximately 30 iterations.
  • Total travel cost decreased with increased fairness index, validating the model's effectiveness.

Conclusions:

  • The proposed lane planning model and algorithms effectively optimize traffic flow for urban connected vehicles.
  • The research contributes to improved operational safety and efficiency of urban transportation systems.
  • This work enhances the current traffic situation in urban networked environments.