Complex Network Study on Urban Rail Transit Systems

Journal of Transportation Systems Engineering and Information Technology ›› 2017, Vol. 17 ›› Issue (6): 242-247.

Complex Network Study on Urban Rail Transit Systems

FENG Jia^{1, 2}, XU Qi ¹, LI Xia-miao ², YANG Yuan-zhou ³

1. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China; 2. School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China; 3. Ministry of Transport of the People’s Republic of China, Beijing 100736, China

Received:2017-06-22 Revised:2017-08-30 Online:2017-12-25 Published:2017-12-25

城市轨道交通系统网络复杂性研究

冯佳^{1, 2}，许奇* ¹，李夏苗²，杨远舟³

1. 北京交通大学城市交通复杂系统理论与技术教育部重点实验室，北京100044； 2. 中南大学交通运输工程学院，长沙410075；3. 交通运输部，北京100736

作者简介:冯佳（1987-），女，辽宁沈阳人，博士后.
基金资助:
国家自然科学基金/ National Natural Science Foundation of China (71621001, 71390332)；中国博士后科学基金/ China Postdoctoral Science Foundation Funded Project(2015M582347)；中央高校基本科研业务费专项基金/ The Fundamental Research Funds for the Central Universities (2016JBZ001, KTRC16004536).

Abstract

Abstract:

The rail transit systems have been paid much attention. The studies of rail transit systems based on complex network theory are extending from single static topology structure to multilayer network dynamic distribution characteristics. Based on the presentation and performance indicators, we conclude the important characteristics of topological structure and traffic flows. The important topological patterns present as small world, hierarchy and the power- law distribution of distance between stations. The traffic flow emerge power- law behavior quantity distribution and spatial- temporal cluster. The multilayer network models show that the load fit a power-law distribution.

Key words: railway transportation, urban rail transit, complex network, topological structure, traffic flow

摘要：

随着交通拥堵的加剧，轨道交通以其快速、大运量的特点得到了青睐，其运营里程迅猛增加且在一定范围内呈现网络化运营趋势.轨道交通网络复杂性研究正在经历从单一网络静态拓扑结构扩展至多层网络动态分配特征的深入过程.在介绍拓扑结构与交通流网络的表示方法和网络复杂性评价指标的基础上，归纳轨道交通网络具有小世界、层次性及站间距幂律分布等静态拓扑特性，呈现明显幂律行为的数量分布及时空集聚等交通流特征.针对轨道交通系统的多层网络研究表明网络负载基本符合幂律分布.研究深化了对轨道交通运行机理的认知，有助于真实网络的预测和控制工作，为探索轨道交通网络化运营、一体化发展条件下客流预测、运力与需求匹配提供了参考方向.

关键词: 铁路运输, 城市轨道交通, 复杂网络, 拓扑结构, 交通流

CLC Number:

FENG Jia, XU Qi, LI Xia-miao, YANG Yuan-zhou. Complex Network Study on Urban Rail Transit Systems[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(6): 242-247.

冯佳，许奇，李夏苗，杨远舟. 城市轨道交通系统网络复杂性研究[J]. 交通运输系统工程与信息, 2017, 17(6): 242-247.

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References

[1] LATORA V, MARCHIORI M. Efficient behavior of small-world networks[J]. Physical Review Letters, 2001, 87(19): 198701, 1-4.

[2] LATORA V, MARCHIORI M. Is the Boston subway a small-world network? [J]. Physica A, 2002, 314(1-4): 109-113.

[3] SIENKIEWICZ J, HOLYST J A. Statistical analysis of 22 public transport networks in Poland[J]. Physical Review E, 2005(72): 046127.

[4] SEN P, DASGUPTA S, CHATTERJEE A, et al. Smallworld properties of the Indian railway network[J]. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2003, 67(3 Pt 2): 036106.

[5] LIN J Y, BAN Y F. Complex network topology of transportation systems[J]. Transport Reviews: A Transnational Transdisciplinary Journal, 2013, 33(6): 658-685.

[6] KURANT M, THIRAN P. Trainspotting: Extraction and analysis of traffic and topologies of transportation networks[J]. Physical Review E, 2006(74): 036114.

[7] 中华人民共和国住房和城乡建设部. 城市轨道交通工程基本术语标准GB/T 50833-2012[S]. 北京: 中国建筑工业出版社, 2012. [Ministry of Housing and Urban- Rural Development of the People’s Republic of China. Standard for basic terminology of urban rail transit engineering GB/T 50833- 2012[S]. Beijing: China Construction Industry Press, 2012.]

[8] RAMLI M A, MONTEROLA C P, KHOON G L K, et al. A method to ascertain rapid transit systems’throughput distribution using network analysis[J]. Procedia Computer Science, 2014(29): 1621-1630.

[9] BARRAT A, BARTHELEMY M, VESPIGNANI A. Modeling the evolution of weighted networks[J]. Physical Review E, 2004(70): 066149.

[10] WATTS D J, STROGATZ S H. Collective dynamics of “small- world”networks[J]. Nature, 1998(393): 440- 442.

[11] BARRAT A, BARTHELEMY M, PASTOR-SATORRAS R, et al. The architecture of complex weighted networks[C]. Proceedings of the National Academy of Sciences of the United States of America, 2004.

[12] VRAGOVI? I, LOUIS E, DIAZ-GUILERA A. Efficiency of informational transfer in regular and complex networks[J]. Physical Review E, 2005, 71(3): 036122.

[13] LENG B, ZHAO X X, XIONG Z. Evaluating the evolution of subway netorks: Evidence from Beijing subway network[J]. Europhysics Letters, 2014(105): 58004.

[14] ROTH C, KANG S M, BATTY M, et al. A long-time limit for world subway networks[J]. Journal of the Royal Society Interface, 2012, 9(75): 2540.

[15] VON FERBER C, HOLOVATCH Y. Fractal transit networks: self-avoiding walks and Lévy flights[J]. The European Physical Journal Special Topics, 2013, 216 (1): 49-55.

[16] FENG J, LI X, MAO B, et al. Weighted complex network analysis of the Beijing subway system: train and passenger flows[J]. Physica A, 2017(474): 213–223.

[17] YAN X Y, HAN X P, WANG B H, et al. Diversity of individual mobility patterns and emergence of aggregated scaling laws[J]. Scientific Reports, 2013(3): 2678.

[18] 毛保华, 等. 城市轨道交通系统运营管理(第二版)[M]. 北京: 人民交通出版社, 2017. [MAO B H, et al. Operations and management for urban rail transit(2nd Edition) [M]. Beijing: China Communications Press, 2017.]