基于Space-P复杂网络模型的城市公交网络特性分析

doi:10.11896/j.issn.1002-137X.2018.08.022

Abstract

Abstract: For the overall performance analysis of transfer network in urban public transport networks,an analysis method based on complex network theory was proposed.Firstly,the public network is modeled as a public transport network topology model represented by Space-P method based on the idea of graph theory.Then,the degree distribution,the average shortest path length,the clustering coefficient,the closeness centrality and the betweenness centrality of the transport network are analyzed statistically.This paper took the public bus network in Beijing as an example.It shows that the Beijing public transport network has the characteristics of small-world network.The probability of transfer is bigger,but the transfer is more convenient.At the same time,the specific geographical information of the re-levant stations was given,which can provide reference for the public transportation planning department to optimize the public transportation network.

Key words: Complex network, Small-world character, Space-P transfer network, Urban public transport

CLC Number:

TP311

KONG Fan-yu, ZHOU Yu-feng, LI Xian-zhong. Characteristic Analysis of Urban Public Transport Networks Based on Space-P Complex Network Model[J].Computer Science, 2018, 45(8): 125-130.

References

[1]LIANG Y,MA Q S,XU P.Sneak circuit partition analysis me-thod based on graph theory [J].Journal of Beijing University of Aeronautics and Astronautics,2014,40(1):115-119.(in Chinese)梁因,马齐爽,徐萍.基于图论的潜通路分块分析方法[J].北京航空航天大学学报,2014,40(1):115-119.
[2]CHAO Y C,JI Z J,WANG Y W,et al.Necessary and sufficient conditions for the controllability of complex networks with path topology[J].CAAI Transactions on Intelligent Systems,2015,10(4):577-582.(in Chinese)晁永翠,纪志坚,王耀威,等.复杂网络在路形拓扑结构下可控的充要条件[J].智能系统学报,2015,10(4):577-582.
[3]OSTILLI M,FERREIRA A L,MENDES J F.Critical behavior and correlations on scale-free small-world networks:application to network design[J].Physical Review E Statistical Nonlinear &Soft Matter Physics,2011,83(1):149-155.
[4]ZHANG Y,ZHANG Q,QIAO J.Analysis of Guangzhou metro network based on L-space and P-space using complex network[C]∥International Conference on Geoinformatics.IEEE:Piscataway,NJ,2014:1-6.
[5]XU P P,SHAO C F.A RLP Modeling and Complexity Analysis on Urban Transit Network[J].Journal of Wuhan University of Technology(Transportation Science & Engineering),2016,40(2):321-325.(in Chinese)徐佩佩,邵春福.城市公共交通网络RLP建模及复杂性分析[J].武汉理工大学学报(交通科学与工程版),2016,40(2):321-325.
[6]WANG T,CHEN J.Space P-Based Urban Public Transit Complex Network Characteristics of Different-Scale Cities[C]∥Cota International Conference of Transportation Professionals.IEEE:Piscataway,NJ,2016:913-923.
[7]WATTS D J,STROGATZ S H.collective dynamics of ‘small world’ networks[J].Nature,1998,393(5):440-442.
[8]BARABASI A L,ALBERT R.Emergence of Scaling in Random Networks[J].Science,1999,286(9):509-512.
[9]ZHANG H,ZHAO P,GAO J,et al.The Analysis of the Properties of Bus Network Topology in Beijing Basing on Complex Networks[J].Mathematical Problems in Engineering,2012,10(1):127-148.
[10]ZHANG N,MAO G.A Multilevel Simplification Algorithm forComputing the Average Shortest-Path Length of Scale-Free Complex Network[J].Journal of Applied Mathematics,2014,26(4):1-6.
[11]LIU H,ZHOU G G,FU P H.Local Evolving Model Research of Layered Supply Chains Complex Networks[J].Computer Scien-ce,2013,40(2):270-273.(in Chinese)柳虹,周根贵,傅培华.分层供应链复杂网络局部演化模型研究[J].计算机科学,2013,40(2):270-273.
[12]XU Q,ZU Z H,XU Z J ,et al.Space P-Based Empirical Re-search on Public Transport Complex Networks in 330 Cities of China[J].Journal of Transportation Systems Engineering & Information Technology,2013,13(1):193-198.
[13]WEHMUTH K,ZIVIANI A.Distributed assessment of thecloseness centrality ranking in complex networks[C]∥The Workshop on Simplifying Complex Networks for Practitioners.ACM:New York,NY,2012:43-48.
[14]TIAN Y,LIU Z G.Detecting Most Influential Nodes in Complex Networks by KSN Algorithm [J].Computer Science,2015,42(11A):296-300.(in Chinese)田艳,刘祖根.利用KSN算法发现网络中有影响力的结点[J].计算机科学,2015,42(11A):296-300.
[15]HU P,FAN W L.Invulnerability of urban transit network under different attack modes[J].Application Research of Computers,2014,31(11):3385-3391.(in Chinese)胡萍,范文礼.不同攻击模式下城市公交网络抗毁性分析[J].计算机应用研究,2014,31(11):3385-3391.
[16]SUN D H,FU Q S,LI Y F.Forecasting of Public Traffic Passenger Volume Based on Quantum Neural Network.Journal of Chongqing University of Technology(Natural Science),2011,25(2):96-99,111.(in Chinese)孙棣华,付青松,李永福.基于量子神经网络的公交客运量预测[J].重庆理工大学学报(自然科学),2011,25(2):96-99,111.

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Characteristic Analysis of Urban Public Transport Networks Based on Space-P Complex Network Model

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