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The escalating urbanization and increasing vehicular density in modern cities demand innovative solutions to manage traffic efficiently and mitigate congestion-related challenges. This paper introduces a simple and efficient approach to developing an adaptive traffic control system using RTX Real-Time Operating Systems (RTOS). The work involves the formulation of an algorithm. The proposed system leverages real-time data from sensors to dynamically adjust traffic signal timings in response to varying traffic densities. To implement the system, a network of infrared (IR) sensors is deployed at strategic locations, collecting data on traffic densities. This information is processed in real time by the Cortex M3 microcontroller with the RTX RTOS kernel, applying suitable optimization techniques. The control signals thus generated are used to dynamically alter signal timings. The system is designed to be scalable, allowing for seamless integration with existing traffic infrastructure.

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Prototype Development and Testing of an Adaptive Real-Time Operating System-Based Density-Oriented Traffic Signal Control

  • 会议论文

  • 发表日期：

    2025年

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  • Pradhangouda Patil,
  • Srujan Hiremath,
  • Punit Mudishennavar,
  • Prakash Bandi,
  • Anupama R. Itagi,
  • Jayashree Mallidu,
  • Anupkumar Patil

摘要

The escalating urbanization and increasing vehicular density in modern cities demand innovative solutions to manage traffic efficiently and mitigate congestion-related challenges. This paper introduces a simple and efficient approach to developing an adaptive traffic control system using RTX Real-Time Operating Systems (RTOS). The work involves the formulation of an algorithm. The proposed system leverages real-time data from sensors to dynamically adjust traffic signal timings in response to varying traffic densities. To implement the system, a network of infrared (IR) sensors is deployed at strategic locations, collecting data on traffic densities. This information is processed in real time by the Cortex M3 microcontroller with the RTX RTOS kernel, applying suitable optimization techniques. The control signals thus generated are used to dynamically alter signal timings. The system is designed to be scalable, allowing for seamless integration with existing traffic infrastructure.