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Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Control: Model Enhancement and Testing on Isolated

Amr K Shafik1, Hesham A Rakha1

  • 1Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24061, USA.

Sensors (Basel, Switzerland)
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PubMed
Summary

This study introduces an enhanced Decentralized Nash Bargaining (DNB) traffic signal controller. It significantly reduces vehicle delay and queue size, outperforming existing methods without requiring pre-training for adaptive traffic control.

Keywords:
Nash bargainingadaptive signal controlcycle length optimizationgame theoryintelligent transportation systemssignal timingtraffic signal control

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Area of Science:

  • Traffic Engineering
  • Intelligent Transportation Systems
  • Game Theory

Background:

  • Traffic signal control systems are crucial for urban mobility.
  • Existing methods like fixed-time and actuated controllers have limitations in dynamic traffic conditions.
  • Adaptive traffic signal control aims to optimize traffic flow in real-time.

Purpose of the Study:

  • To enhance and evaluate a Decentralized Nash Bargaining (DNB) adaptive traffic signal controller.
  • To improve signal timing optimization using traffic density estimates.
  • To integrate the National Electrical Manufacturers Association (NEMA) controller configuration into a game-theoretic framework.

Main Methods:

  • The enhanced DNB controller was developed using game-theoretic principles.
  • It incorporates traffic density estimates, NEMA phasing, and adaptable control time steps.
  • Performance was benchmarked against fixed-time (Webster, LDR), actuated, and reinforcement learning (RL) controllers.

Main Results:

  • The DNB controller demonstrated superior performance over pretimed and actuated controllers.
  • It achieved up to a 54% reduction in average vehicle delay and a 63% reduction in queue size compared to Webster's method.
  • The DNB controller outperformed a previously developed RL controller.

Conclusions:

  • The enhanced DNB controller offers a flexible and effective framework for reducing traffic congestion.
  • It adapts dynamically to fluctuating traffic demands without pre-training.
  • This research contributes to developing more responsive urban traffic control systems.