[1]彭雅丽,杨雨鑫,曾欣怡,等.一种构建波动模态组的复杂网络分析方法研究[J].江西师范大学学报(自然科学版),2019,(03):298-308.[doi:10.16357/j.cnki.issn1000-5862.2019.03.14]
　PENG Yali,YANG Yuxin,ZENG Xinyi,et al.The Research on a Complex Network Analysis Method for Constructing Waveform Modes[J].Journal of Jiangxi Normal University:Natural Science Edition,2019,(03):298-308.[doi:10.16357/j.cnki.issn1000-5862.2019.03.14]
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一种构建波动模态组的复杂网络分析方法研究()
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《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年03期
页码:
298-308
栏目:
信息科学与技术
出版日期:
2019-06-10

文章信息/Info

Title:
The Research on a Complex Network Analysis Method for Constructing Waveform Modes
文章编号:
1000-5862(2019)03-0298-11
作者:
彭雅丽; 杨雨鑫; 曾欣怡; 邓建刚
江西师范大学软件学院,江西 南昌 330022
Author(s):
PENG Yali; YANG Yuxin; ZENG Xinyi; DENG Jiangang
College of Software, Jiangxi Normal University, Nanchang Jiangxi 330000, China
关键词:
复杂网络;  时间序列;  存量波动;  模态化;  动态调度
Keywords:
complex network;  time series;  stock fluctuation;  modal;  dynamic scheduling
分类号:
TP 311
DOI:
10.16357/j.cnki.issn1000-5862.2019.03.14
文献标志码:
A
摘要:
以公共自行车实际数据为基础,将其布点的存量时间序列转换为有限的模态序列,以进一步进行时间序列波动性分析,并提取序列的关键特征,构建波动模态组复杂网络.运用一种基于滑动窗口及模态参数的分析法进行公共自行车存量特征提取,为系统优化提供依据.实证研究表明:所建网络特征与滑动窗口大小、网络的出度、加权聚类系数相关,通过选择最优参数,可为优化公共自行车系统调度时间提出可行、高效的解决方案.
Abstract:
Based on the actual data of public bicycles,the stock time series data of the distribution points is transformed into finite modal sequences to further analyze the time series volatility and extract the key features of the sequence,and the complex network of the fluctuating modal group is constructed.Based on the analysis of sliding window and modal parameters,the stock collection of public bicycle is taken to provide the basis for system optimization.The empirical study shows that the network characteristics are related to the size of the sliding window,the degree of the network,and the weighted clustering coefficient.By choosing the optimal parameters,a feasible and efficient solution for optimizing the scheduling time of the common bicycle system is proposed.

参考文献/References:

[1] 吴建军,李树彬.基于复杂网络的城市交通系统复杂性概述[J].山东科学,2009,22(4):68-73.
[2] 李树彬,吴建军,高自友,等.基于复杂网络的交通拥堵与传播动力学分析[J].物理学报,2011,60(5):140-148.
[3] 张敏捷,徐建闽,蔡延光.基于改进宏观交通模型的交通协调控制[J].华南理工大学学报:自然科学版,2013,41(4):83-89.
[4] 赵晖.一般输运网络演化模型及动力学特征的相关研究[D].北京:北京交通大学,2007.
[5] 连爱萍,高自友,龙建成.基于路段元胞传输模型的动态用户最优配流问题[J].自动化学报,2007,33(8):852-859.
[6] 汪秉宏.城市交通系统的时空复杂性与结构瓶颈演化[J].中国科技成果,2014(2):52-54.
[7] Wang Binghong,Wang Wenxu.Routing strategies in traffic network and phase transition in network traffic flow[J].Pramana,2009,71(2):353-358.
[8] Yan Gang,Zhou Tao,Hu Bo,et al.Efficient routing on complex networks[J].Phys Rev,2005,73(4):46-58.
[9] Shu Jia,Chou Mabel C,Liu Qizhang,et al.Models for effective deployment and redistribution of bicycles within public bicycle-sharing systems[J].Operations Research,2013,61(6):1346-1359.
[10] Erdogan G,Laporte G,Calvo R W.The one-commodity pickup and delivery traveling salesman problem with demand intervals[EB/OL].[2016-11-15].http://www.cirrelt.ca/DocumentsTravail/CIRRELT-2013-46.pdf
[11] Labadi K,Darcherif A M,Hamaci S,et al.Petri nets models for analysis and control of public bicycle-sharing systems[M]∥Pawelewski P.Petri nets:manufacturing and computer science.London:InTech,2012.
[12] Gang Anhai,Du Qinjun,Zhang Yongli.Study on univariate volatility of time series based on complex network[J].System Science and Mathematics,2015,35(2):158-169.
[13] Sun Miao,Kong Xiangchao,GengWeihua.Time series analysis on month precipitation in Shandong province based on ARIMA model[J].Ludong University Journal:Natural Science Edition,2013,29(3):244-249.
[14] Gao Xiangyun,An Haizhong,Fang Wei.Study on bivariate correlation fluctuation of time series based on complex networks[J].Acta Physica Sinica,2012,61(9):535-543.
[15] 管宇.数据挖掘中一种新的有序聚类方法[J].中国管理科学,2011,19(专辑):74-78.

备注/Memo

备注/Memo:
收稿日期:2019-01-27
基金项目:国家自然科学基金(71661015)资助项目.
作者简介:彭雅丽(1983-),女,江西吉安人,副教授,主要从事无线传感器网络和智能交通的研究.E-mail:29917263@qq.com
更新日期/Last Update: 2019-06-10