Evaluating and Diagnosing Road Intersection Operation Performance Using Floating Car Data.

Area of Science:

• Urban planning and transportation engineering.
• Traffic flow analysis and network optimization.

Background:

• Urban road intersections are critical determinants of overall travel time and efficiency.
• Existing methods for diagnosing intersection performance often lack comprehensive network-wide analysis.

Purpose of the Study:

• To propose an innovative traffic grid model for evaluating and diagnosing traffic status and time delays at urban intersections.
• To identify intersections with the longest traffic delays and analyze contributing factors like signal timing.

Main Methods:

• Development of a grid model to discretize intersections and assign floating car data.
• Derivation of key traffic parameters including total time delay and average speed per direction.
• Evaluation of intersections based on derived parameters, traffic flow ratio, and green time ratio.

Main Results:

• Demonstrated feasibility and potential of the traffic grid model using a case study in Beijing.
• Successfully identified intersections with the longest delays and detected unreasonable signal timing phases.
• The model provides actionable insights for traffic management and operational efficiency improvements.

Conclusions:

• The proposed traffic grid model effectively evaluates and diagnoses urban signal intersections.
• This method offers a practical tool for traffic managers to reduce delays and enhance intersection efficiency.
• The model's adaptability allows for easy transfer to other cities for widespread application.