Multiobjective optimization model of intersection signal timing considering emissions based on field data: A case...

Area of Science:

• Environmental Science
• Transportation Engineering
• Traffic Management

Background:

• Traditional traffic signal timing models prioritize minimizing vehicle delay and stops.
• These models often overlook the significant environmental impact of vehicle emissions at urban intersections.
• Field data collection is crucial for understanding emission rates across different vehicle operating modes.

Purpose of the Study:

• To analyze the trade-off between vehicle emissions and traffic efficiency at signalized intersections.
• To develop a multiobjective signal timing optimization model incorporating environmental factors.
• To provide a framework for eco-signal design and development.

Main Methods:

• Collected field data on vehicle emissions and Global Positioning System (GPS) data for light-duty and heavy-duty vehicles.
• Estimated emission rates considering various operating modes: cruising, acceleration, deceleration, and idling.
• Developed a multiobjective signal timing optimization model using a genetic algorithm to minimize delay, stops, and emissions.

Main Results:

• A case study in Beijing demonstrated that the proposed model significantly decreases vehicle delay and emissions compared to the Highway Capacity Manual (HCM) 2010.
• Optimized signal timing effectively balances traffic efficiency with environmental considerations.
• The model's effectiveness was validated across nine different scenarios with varying weights for emission and traffic efficiency.

Conclusions:

• The developed multiobjective signal timing optimization model offers a superior approach to traditional methods by integrating environmental factors.
• This model provides a practical tool for reducing vehicle emissions and improving traffic flow in urban environments.
• The approach is adaptable for application in diverse urban settings, supporting the advancement of eco-friendly traffic management strategies.