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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
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Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices.

Sensors (Basel, Switzerland)·2022
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Updated: Jan 9, 2026

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Multi-Agent Adaptive Traffic Signal Control Based on Q-Learning and Speed Transition Matrices.

Željko Majstorović1, Edouard Ivanjko1, Tonči Carić1

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Connected vehicles (CVs) provide real-time traffic data for safer roads. This study introduces adaptive traffic signal control using speed transition matrices and multi-agent learning for improved traffic flow.

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Area of Science:

  • Transportation Engineering
  • Artificial Intelligence
  • Traffic Management Systems

Background:

  • Connected vehicles (CVs) offer real-time microscopic traffic data, enhancing road capacity and safety.
  • Vehicle-to-Everything (V2X) communication enables CVs to act as mobile sensors.
  • Speed Transition Matrices (STMs) can process CV data while preserving spatio-temporal features.

Purpose of the Study:

  • To propose a novel adaptive traffic signal control strategy for connected vehicle environments.
  • To leverage STMs and cooperative multi-agent learning for intelligent traffic management.
  • To evaluate the proposed system's performance under varying CV penetration rates and cooperation levels.

Main Methods:

  • Development of an adaptive traffic signal control system integrating STMs and cooperative multi-agent learning.
  • Simulation of an intersection network environment to test the proposed control strategy.
  • Comparative analysis of system performance across different CV penetration rates and agent cooperation coefficients.

Main Results:

  • The proposed adaptive traffic signal control system demonstrates effectiveness in simulated intersection networks.
  • Performance improvements were observed with increased CV penetration rates and higher agent cooperation.
  • The integration of STMs and multi-agent learning provides a robust approach to traffic signal optimization.

Conclusions:

  • The novel adaptive traffic signal control approach utilizing STMs and cooperative multi-agent learning is effective for CV environments.
  • This research highlights the potential of CVs as mobile sensors for advanced traffic management.
  • Future work can explore real-world deployment and scalability of the proposed system.