基于逾渗理论的交通路网通行效率分析

计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 127-133.

基于逾渗理论的交通路网通行效率分析

高华兵¹, 宋聪聪², 陈波², 刘志²

(宜春职业技术学院信息工程学院江西宜春336000)¹;
(浙江工业大学计算机科学与技术学院杭州310023)²

出版日期:2019-11-10 发布日期:2019-11-20
通讯作者: 刘志(1969-),女,博士,教授,CCF会员,主要研究方向为智能交通、智能计算等,E-mail:lzhi@zjut.edu。
作者简介:高华兵(1977-),男,副教授,主要研究方向为智能交通。
基金资助:
本文受浙江省自然科学基金(LY16F020033,LY16F020035)资助。

Traffic Efficiency Analysis of Traffic Road Network Based on Percolation Theory

GAO Hua-bing¹, SONG Cong-cong², CHEN Bo², LIU Zhi²

(College of Information Engineering,Yichun Vocational Technical College,Yichun,Jiangxi 336000,China)¹;
(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)²

Online:2019-11-10 Published:2019-11-20

摘要/Abstract

摘要： 针对城市路网的拥堵现象,利用逾渗理论对路网模型的通行效率进行分析。首先,利用实际城市道路的地理数据,应用原始法来构建交通路网模型;然后,对路网通行效率进行量化计算,分析在不同天气状况下拥堵路段对交通态势的影响。文中主要通过路网规则的制定、阈值的分析、强连通子图的划分和通行效率的计算来实现对交通态势的评估,并在不同的天气状况下验证天气因素对交通路网的影响。

关键词: 复杂网络, 强连通子图, 通行效率, 逾渗理论

Abstract: According to the congestion phenomenon of urban road network,the percolation theory is used to analyze the traffic efficiency of road network model.Firstly,by using the geographic data of actual urban roads,the original method is used to construct the traffic road network model.Then,by quantifying the traffic efficiency of the road network,the influence of the traffic jams on the traffic situation under different weather conditions is analyzed.This paper mainlyevaluated the traffic situation through the formulation of rules,the analysis of thresholds,the division of strong connec-ted subgraphs and the determination of traffic efficiency.The influence of weather factors on traffic network was verified under different weather conditions.

Key words: Complex networks, Percolation theory, Strongly connected subgraph, Traffic efficiency

中图分类号:

TP391

高华兵, 宋聪聪, 陈波, 刘志. 基于逾渗理论的交通路网通行效率分析[J]. 计算机科学, 2019, 46(11A): 127-133. https://doi.org/

GAO Hua-bing, SONG Cong-cong, CHEN Bo, LIU Zhi. Traffic Efficiency Analysis of Traffic Road Network Based on Percolation Theory[J]. Computer Science, 2019, 46(11A): 127-133. https://doi.org/

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