<p>This study introduces a comprehensive mathematical model designed to optimize traffic flow in a newly planned Organized Industrial Zone (OIZ). The proposed model focuses on four primary objectives: maximizing overall traffic flow, minimizing congestion, regulating peak-hour traffic, and effectively managing personnel and visitor traffic. The model incorporates traffic demand, road and intersection capacities, and vehicle types, leveraging these parameters to ensure efficient and sustainable traffic management. Unlike traditional simulation-based approaches, this model integrates optimization techniques to address the unique challenges of OIZs, such as logistics flows and personnel movements. Key components include decision variables for vehicle flows across road sections, robust capacity constraints for roads and intersections, and weighted parameters to balance objectives. The model also considers infrastructure improvements like road expansions, intersection adjustments, and traffic signal optimization. By optimizing traffic flow under various constraints, the model offers a strategic planning tool for decision-makers, enabling data-driven adjustments to infrastructure and traffic policies. Its adaptable structure allows for application in diverse OIZ projects, enhancing both operational efficiency and long-term sustainability. The study highlights the model’s potential to serve as a benchmark for OIZ traffic management, addressing critical gaps in existing literature.</p>

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An innovative mathematical model for integrated traffic flow optimization in organized industrial zones

  • Hayri Ulvi,
  • Mehmet Akif Yerlikaya,
  • Kürşat Yildiz

摘要

This study introduces a comprehensive mathematical model designed to optimize traffic flow in a newly planned Organized Industrial Zone (OIZ). The proposed model focuses on four primary objectives: maximizing overall traffic flow, minimizing congestion, regulating peak-hour traffic, and effectively managing personnel and visitor traffic. The model incorporates traffic demand, road and intersection capacities, and vehicle types, leveraging these parameters to ensure efficient and sustainable traffic management. Unlike traditional simulation-based approaches, this model integrates optimization techniques to address the unique challenges of OIZs, such as logistics flows and personnel movements. Key components include decision variables for vehicle flows across road sections, robust capacity constraints for roads and intersections, and weighted parameters to balance objectives. The model also considers infrastructure improvements like road expansions, intersection adjustments, and traffic signal optimization. By optimizing traffic flow under various constraints, the model offers a strategic planning tool for decision-makers, enabling data-driven adjustments to infrastructure and traffic policies. Its adaptable structure allows for application in diverse OIZ projects, enhancing both operational efficiency and long-term sustainability. The study highlights the model’s potential to serve as a benchmark for OIZ traffic management, addressing critical gaps in existing literature.