Connectivity Reliability on an Urban Rail Transit Network from the Perspective of Passenger Travel

Jie Liu , Qiyuan Peng , Jinqu Chen , Yong Yin

Urban Rail Transit ›› 2020, Vol. 6 ›› Issue (1) : 1 -14.

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Urban Rail Transit ›› 2020, Vol. 6 ›› Issue (1) : 1 -14. DOI: 10.1007/s40864-019-00117-z
Original Research Papers

Connectivity Reliability on an Urban Rail Transit Network from the Perspective of Passenger Travel

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Abstract

Under the background of urbanization and the rapid development of urban rail transit (URT), serious attention has been focused on URT network reliability in recent years. In this work, in order to measure network reliability, three indicators are constructed based on passengers’ tolerable travel paths, passenger travel efficiency and passenger travel realization on a URT network. The passenger tolerability coefficient, which is the ratio of passengers’ tolerable travel time to the shortest possible travel time, is proposed and added to the indicators. It reflects passengers’ behavior with respect to choice of travel paths. The ratio of affected passenger volume (RAPV) is proposed to identify important stations. Finally, the connectivity reliability of Wuhan’s subway network is analyzed by simulating attacks on stations. The results show that the degree centrality, betweenness centrality and RAPV indicators of stations can effectively identify the important stations that have a significant impact on the connectivity reliability of the network. In particular, the RAPV indicator effectively identifies stations that have the greatest influence on passenger travel realization. The connectivity reliability of Wuhan’s subway network is sensitive to passenger tolerability coefficient, and reliability is greater during non-peak hours than during peak hours. In addition, the stations that are important to the connectivity reliability of the Wuhan subway have two features, i.e., they are located at the center of the city, and they are important for connecting subgraphs of the network.

Keywords

Urban rail transit network / Connectivity reliability / Passenger travel / Tolerable travel paths / Travel efficiency / Travel realization

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Jie Liu, Qiyuan Peng, Jinqu Chen, Yong Yin. Connectivity Reliability on an Urban Rail Transit Network from the Perspective of Passenger Travel. Urban Rail Transit, 2020, 6(1): 1-14 DOI:10.1007/s40864-019-00117-z

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References

[1]

Latora V, Marchiori M. Is the Boston subway a small-world network?. Physica A, 2002, 314(1–4): 109-113

[2]

Derrible S, Kennedy C. The complexity and robustness of metro networks. Physica A, 2010, 389(17): 3678-3691

[3]

Seaton KA, Hackett LM. Stations, trains and small-world networks. Physica A, 2004, 339(3–4): 635-644

[4]

Hosseini S, Wadbro E. Connectivity reliability in uncertain networks with stability analysis. Expert Syst Appl, 2016, 57: 337-344

[5]

Zhang YS, Yao EJ. Splitting travel time based on AFC data: estimating walking, waiting, transfer, and in-vehicle travel time in metro system. Discrete Dyn Nat Soc, 2015, 2015: 1-11.

[6]

Du MQ, Jiang XW, Cheng L Robust evaluation for transportation network capacity under demand uncertainty. J Adv Transp, 2017, 2017: 1-11.

[7]

Mine H, Kawai H. Mathematics for reliability analysis, 1982, Tokyo: Asakura-shorten

[8]

Bell MGH, Iida Y. Transportation network analysis, 1997, Chichester: Wiley

[9]

Wakabayashi H, Iida Y. Upper and lower bounds of terminal reliability in road networks: an efficient method with Boolean algebra. J Nat Disaster Sci, 1992, 14: 29-44.

[10]

Jiang CZ, Xu F, Yuan JX. Characteristics and reliability analysis of the complex network in Guangzhou rail transit. Intell Autom Soft Comput, 2013, 19(2): 217-225

[11]

Liu ZQ, Song R. Reliability analysis of Guangzhou rail transit with complex network theory. J Transp Syst Eng Inf Technol, 2010, 10(5): 194-200.

[12]

Wang ZQ, Xu RH. Reliability simulation analysis of urban rail transit networks based on complex network. J Syst Simul, 2009, 21(20): 6670-6674.

[13]

Zhang JH, Hong L, Wang SL, et al (2011) Reliability assessments of Chinese highspeed railway network. In: IEEE international conference on intelligent rail transportation, Beijing, China

[14]

Zhang X, Miller-Hooks E, Denny K. Assessing the role of network topology in transportation network resilience. J Transp Geogr, 2015, 46: 35-45

[15]

Mattsson LG, Jenelius E. Vulnerability and resilience of transport systems – A discussion of recent research. Transp Res Part A, 2015, 81: 16-34.

[16]

Zhang X, Jia L, Dong H, et al (2009) Analysis and evaluation of connectivity reliability for dynamic transportation network. In: 2009 Fifth international joint conference on INC, IMS and IDC. Seoul, South Korea

[17]

Liu J, Lu H, Ma H Network vulnerability analysis of rail transit plans in Beijng-Tianjin-Hebei region considering connectivity reliability. Sustainability, 2017, 9: 1479

[18]

Guidotti R, Gardoni P, Chen Y. Network reliability analysis with link and nodal weights and auxiliary nodes. Struct Saf, 2017, 65: 12-26

[19]

Li M, Jia L, Wang Y (2015). Research and implementation on connectivity reliability calculation algorithm of Urban Rail Transit network operation. In: Proceeding of the 11th world congress on intelligent control and automation, Shenyang, China

[20]

Reggiani A, Nijkamp P, Lanzi D. Transport resilience and vulnerability: the role of connectivity. Transp Res Part A, 2015, 81: 4-15.

[21]

Liu J, Xiong Q, Shi W Evaluating the importance of nodes in complex networks. Phys A, 2016, 452: 209-219

[22]

Rodriguez-Nunez E, Garcia-Palomares JC. Measuring the vulnerability of public transport networks. J Transp Geogr, 2014, 35: 50-63

[23]

Hu P, Fan W, Mei S. Identifying node importance in complex networks. Phys A, 2015, 429: 169-176

[24]

Liu J, Zhou X. Capacitated transit service network design with boundedly rational agents. Transp Res Part B Methodol, 2016, 3: 225-250

[25]

Shang P, Li R, Guo J Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: a space-time-state hyper network-based assignment approach. Transp Res Part B Methodol, 2019, 121: 35-167

[26]

Zhu W, Hu H, Xu R Modified stochastic user-equilibrium assignment algorithm for urban rail transit under network operation. J Cent South Univ, 2013, 20(10): 2897-2904

[27]

Han B, Zhou W, Li D Dynamic schedule-based assignment model for urban rail transit network with capacity constraints. Sci World J, 2015, 2015: 1-12.

[28]

Wardman M, Whelan G. Twenty years of rail crowding valuation studies: evidence from lessons from British experience. Transp Rev, 2011, 31(3): 379-398

[29]

Zhang Y, D’Ariano A, He B, Peng Q. Microscopic optimization model and algorithm for integrating train timetabling and track maintenance task scheduling. Transp Res Part B Methodol, 2019, 127: 237-278

[30]

Zhang Y, Peng Q, Yao Y, Zhang X, Zhou X. Solving cyclic train timetabling problem through model reformulation: extended time-space network construct and alternating direction method of multipliers methods. Transp Res Part B Methodol, 2019, 128: 344-379

[31]

Zhong Q, Lusby RM, Larsen J, Zhang Y, Peng Q. Rolling stock scheduling with maintenance requirements at the Chinese high-speed railway. Transp Res Part B Methodol, 2019, 126: 24-44

Funding

National Key R & D Program of China(2017YFB1200700)

National Natural Science Foundation of China(U1834209)

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