Multi-scale aggregation mode analysis method for complex traffic network

Abstract

The invention provides a multi-scale aggregation mode analysis method for a complex traffic network. The method comprises the following steps: firstly, calculating an adjacent matrix, a position attribute matrix, a distance weight matrix, a road grade matrix and a time-phased traffic congestion degree matrix of a road traffic network; secondly, adding a road network weight influence factor on the basis of an original PageRank algorithm to obtain an improved PageRank algorithm so as to determine a key node sorting condition; and finally, drawing a two-dimensional decision diagram through two indexes of key node sorting and shortest path distance, determining a spectral clustering center point and a clustering number, and adding a position, distance, road grade and dynamic traffic congestion degree weight matrix on the basis of a similarity matrix in spectral clustering to obtain a new weighting matrix conforming to an actual road network condition, and then carrying out clustering analysis to obtain a road network aggregation block. According to the method, the block aggregation characteristics of the road traffic network crossing the administrative division can be analyzed, and decision reference is provided for traffic planning, design work, maintenance work and the like.

Description

technical field [0001] The invention relates to the technical field of highway traffic networks, in particular to a method for analyzing multi-scale aggregation patterns of complex traffic networks. Background technique [0002] The highway transportation network is an important infrastructure serving the economy, society and the public, and is the backbone of the comprehensive transportation system. Multi-scale analysis of complex transportation networks, mining functional blocks of network structures, and identifying distribution characteristics of networks from geospatial perspectives are important aspects of analyzing highway transportation networks. [0003] At present, there are few studies on multi-scale analysis of complex traffic networks. Yang Pan et al. proposed that scale is the basic feature of the objective world (Yang Pan. Multi-scale research on urban furniture color -- taking Hefei as an example [D]. Hefei University of Technology, 2019.), multi-scale resea...

AI Technical Summary

Problems solved by technology

However, the road network data is a typical high-dimensional data, and the use of k-means clustering algorithm to process high-dimensional data is not dominant, resulting in certain limitations in the research results

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