Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research.

Area of Science:

• Transportation Engineering
• Data Science
• Predictive Modeling

Background:

• Traffic flow prediction is crucial for effective routing selection.
• Existing methods like linear, nonlinear, neural networks, and Time Series Analysis have limitations.
• Understanding city traffic flow characteristics is essential for improving prediction accuracy.

Purpose of the Study:

• To develop an advanced road traffic flow prediction method.
• To address the shortcomings of traditional traffic forecasting algorithms.
• To enhance the accuracy and efficiency of real-time traffic flow prediction.

Main Methods:

• Analysis of existing traffic flow prediction algorithms and city traffic characteristics.
• Proposal of a novel method based on transfer probability from upstream roads.
• Utilizing a traffic flow equation and the Newton Interior-Point Method for parameter optimization.

Main Results:

• The proposed transfer probability model demonstrates superior performance compared to existing methods.
• Achieved faster optimization of model parameters.
• Exhibited higher prediction accuracy for traffic flow.

Conclusions:

• The developed model provides a more accurate and efficient approach to traffic flow prediction.
• The method is suitable for real-time traffic flow forecasting applications.
• Transfer probability analysis offers a promising direction for intelligent transportation systems.