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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
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Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections.

Kai Gao1,2, Farong Han3, Pingping Dong4

  • 1College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China. kai_g@csust.edu.cn.

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Summary

This study introduces a new model for sensing traffic queue length using vehicle-to-everything (V2X) technology. The model accurately predicts queue lengths in mixed traffic, even with low connected vehicle penetration rates.

Keywords:
BP neural networkconnected vehiclepenetration ratequeue lengthshockwave

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Area of Science:

  • Intelligent Transportation Systems
  • Traffic Engineering
  • Data Science

Background:

  • Connected vehicles generate valuable traffic data, like intersection queue lengths.
  • Existing queue length models suffer from complexity and data redundancy.
  • Vehicle-to-everything (V2X) technology enables advanced traffic sensing capabilities.

Purpose of the Study:

  • To develop a novel, efficient queue length sensing model for intelligent transportation systems (ITS).
  • To address the limitations of existing models by leveraging V2X technology.
  • To improve traffic management and reduce congestion through accurate queue length prediction.

Main Methods:

  • A hybrid model combining shockwave sensing and back propagation (BP) neural network sensing.
  • Shockwave sensing predicts queue length for unconnected vehicles based on connected vehicle data.
  • BP neural network sub-model predicts real-time queue length using historical connected vehicle data.
  • Variable weight combination of sub-model outputs to determine final queue length.

Main Results:

  • Sensing accuracy is directly proportional to the connected vehicle penetration rate.
  • Effective queue length sensing is achievable even in low penetration rate environments.
  • The proposed model outperforms the probability distribution (PD) model in low penetration mixed traffic scenarios.
  • The model demonstrates high performance in mixed traffic with variable penetration rates, requiring no specific penetration rate threshold.

Conclusions:

  • The developed V2X-based queue length sensing model offers improved accuracy and applicability in mixed traffic environments.
  • The model is robust and effective under various connected vehicle penetration rates.
  • This approach provides a more practical solution for real-world traffic management challenges.