Ricci Flow Key Road-Section Detection Method
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Solution Overview
Problem
Existing methods for extracting key road-sections in traffic networks primarily focus on topological structures without considering actual traffic flow distribution and transmission characteristics, leading to inadequate alleviation of traffic congestion.
Innovation Solution
A detection method utilizing Ricci flow, which iteratively adjusts edge weights in a weighted road network to achieve uniform Olivier Ricci curvature, allowing for the identification of key road-sections based on significant weight changes, thereby reflecting actual traffic flow dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing key road-section extraction methods only use topological structure of road network, then the method is simple, but the detection precision is insufficient because actual traffic flow distribution and transmission characteristics are not considered
Solution Approach 1:
The patent transforms the road network into a weighted graph where edge weights represent traffic flow characteristics. It introduces Olivier Ricci curvature as a new parameter to quantify the importance of road sections, and uses Ricci flow to dynamically adjust weights based on traffic transmission characteristics. This parameter transformation enables more precise identification of key road sections by incorporating actual traffic dynamics rather than relying solely on topological structure.
2Measurement precision
If Ricci flow iterative process is used to achieve uniform Olivier Ricci curvature, then the detection precision improves, but the computing time increases
Solution Approach 1:
The patent implements the Ricci flow iterative process with a predetermined number of iterations rather than running until complete convergence. This partial action approach achieves sufficient uniformity of Olivier Ricci curvature values across the network while avoiding the excessive computing time that would result from continuing iterations until perfect convergence is reached. The method balances precision requirements with computational efficiency.
Data Source
AI summary
A detection method of key road-sections based on Ricci flow is provided and includes: building a weighted road network according to static road network data and actual traffic flow data; calculating initial values of Olivier Ricci curvature at different times; obtaining a weight system of making edges of the weighted road network be with a same value of Olivier Ricci curvature by a Ricci flow iterative process; and calculating direction and degree of weight change of each of the edges corresponding to road-sections, and setting a threshold to extract key road-sections. The method solves problems that the existing methods analyze the key road-sections from the topological structure of the road network without fully considering the actual distribution and transmission characteristics of the traffic flow in the network, is simple and easy; and the detection result more meet the traffic distribution and the flow of the actual road-sections.