Structural and robustness properties of smart-city transportation networks

doi:10.1088/1674-1056/24/9/090201

中国物理B ›› 2015, Vol. 24 ›› Issue (9): 90201-090201.doi: 10.1088/1674-1056/24/9/090201

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Structural and robustness properties of smart-city transportation networks

张振刚^a, 丁卓^a, 樊京芳^{b c}, 孟君^c, 丁益民^d, 叶方富^c, 陈晓松^b

a School of Business Administration, South China University of Technology, Guangzhou 510641, China;
b Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China;
c Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
d Faculty of Physics and Electronics, Hubei University, Wuhan 430062, China

收稿日期:2015-05-25 修回日期:2015-06-01 出版日期:2015-09-05 发布日期:2015-09-05
基金资助:
Project supported by the Major Projects of the China National Social Science Fund (Grant No. 11 & ZD154).

Structural and robustness properties of smart-city transportation networks

Zhang Zhen-Gang (张振刚)^a, Ding Zhuo (丁卓)^a, Fan Jing-Fang (樊京芳)^{b c}, Meng Jun (孟君)^c, Ding Yi-Min (丁益民)^d, Ye Fang-Fu (叶方富)^c, Chen Xiao-Song (陈晓松)^b

a School of Business Administration, South China University of Technology, Guangzhou 510641, China;
b Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China;
c Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
d Faculty of Physics and Electronics, Hubei University, Wuhan 430062, China

Received:2015-05-25 Revised:2015-06-01 Online:2015-09-05 Published:2015-09-05
Contact: Meng Jun, Chen Xiao-Song E-mail:mengjun@iphy.ac.cn;chenxs@itp.ac.cn
Supported by:
Project supported by the Major Projects of the China National Social Science Fund (Grant No. 11 & ZD154).

摘要/Abstract

摘要：

The concept of smart city gives an excellent resolution to construct and develop modern cities, and also demands infrastructure construction. How to build a safe, stable, and highly efficient public transportation system becomes an important topic in the process of city construction. In this work, we study the structural and robustness properties of transportation networks and their sub-networks. We introduce a complementary network model to study the relevance and complementarity between bus network and subway network. Our numerical results show that the mutual supplement of networks can improve the network robustness. This conclusion provides a theoretical basis for the construction of public traffic networks, and it also supports reasonable operation of managing smart cities.

关键词: percolation phase transition, finite size scaling theory, network, smart city

Abstract:

Key words: percolation phase transition, finite size scaling theory, network, smart city

中图分类号: (Combinatorics; graph theory)

02.10.Ox

05.70.Fh (Phase transitions: general studies) 64.60.-i (General studies of phase transitions)

张振刚, 丁卓, 樊京芳, 孟君, 丁益民, 叶方富, 陈晓松. Structural and robustness properties of smart-city transportation networks[J]. 中国物理B, 2015, 24(9): 90201-090201.

Zhang Zhen-Gang (张振刚), Ding Zhuo (丁卓), Fan Jing-Fang (樊京芳), Meng Jun (孟君), Ding Yi-Min (丁益民), Ye Fang-Fu (叶方富), Chen Xiao-Song (陈晓松). Structural and robustness properties of smart-city transportation networks[J]. Chin. Phys. B, 2015, 24(9): 90201-090201.

参考文献 22

[1]	Watts D J and Strogatz S H 1998 Nature 393 6684
[2]	Barabási A L and Albert R 1999 Science 286 5439
[3]	Albert R and Barabási A 2002 Rev. Mod. Phys. 74 47
[4]	Newman M 2010 Networks: An Introduction (Oxford University Press) pp. 12-16
[5]	Buldyrev S V, Parshani R, Paul G, Stanley H E and Havlin S 2010 Nature 464 7291
[6]	Cohen R and Havlin S 2010 Complex Networks: Structure, Robustness and Function (Cambridge University Press) p. 25
[7]	Erdős P and Rényi A 1960 Publ. Math. Inst. Hung. Acad. Sci. 5 17
[8]	Erdős P and Rényi A 1959 Publ. Math. Debrecen 6 290
[9]	Albert A, Jeong H and Barabási A L 2000 Nature 406 6794
[10]	Colaiori F, Flammini A, Maritan A and Banavar J R 1997 Phys. Rev. E 55 1298
[11]	Faloutsos M, Faloutsos P and Faloutsos C 1999 ACM SIGCOMM Computer Communication Review 29 256
[12]	Banavar J R, Maritan A and Rinaldo A 1999 Nature 399 6732
[13]	Jeong H, Tombor B, Albert R, Oltvai Z and Barabási A L 2000 Nature 407 6804
[14]	Williams R J and Martinez N D 2000 Nature 404 6774
[15]	Girvan M and Newman M 2002 PNAS 99 7821
[16]	Stauffer D and Aharony A 1994 Introduction to Percolation Theory. (Taylor & Francis) pp. 2-8
[17]	Bollobás B 2001 Random Graphs (Cambridge University Press) pp. 130-143
[18]	Achlioptas D, D'Souza R M and Spencer J 2009 Science 323 5920
[19]	Costa R A, Dorogovtsev S N, Goltsev A V and Mendes J F F 2010 Phys. Rev. Lett. 105 255701
[20]	Riordan O and Warnke L 2011 Science 333 6040
[21]	Fan J F, Liu M X, Li L S and Chen X S 2012 Phys. Rev. E 85 061110
[22]	Fan J F and Chen X S 2014 EPL 107 28005

Structural and robustness properties of smart-city transportation networks

Structural and robustness properties of smart-city transportation networks

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