Research on evolutionary model of urban rail transit vulnerability based on computer simulation
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In order to overcome the vulnerability of the effect of large passenger flow, an improved method based on the vulnerability of large passenger flow was proposed, and 5-year (2013–2017) passenger flow techniques are applied. The urban rail transit safety vulnerability simulation model mainly has three modules. Module 1: Urban rail transit can respond to disturbances in time and make corresponding adjustments and adaptations. Module 2: Urban rail traffic can be restored to a completely normal state for certain disturbances. Module 3: Urban rail transit can be completed within a limited self-recovery and adjustment time, and the fragile state after disturbance can be restored to normal state in time. This section of urban rail transit safety vulnerability evolution model is the core of the algorithm is studied, and according to the model to design the best algorithm procedures, specific algorithm to run the program is shown in Fig. 1. The results of this paper can be used as a basis for solving safety problems. It can in turn help to avoid or reduce the occurrence of disasters and to ensure the safe, fast and efficient operation of subway. This work has significance in theory and practice.
KeywordsPassenger flow Subway Vulnerability Evolutionary Simulation
This research was supported by Institute of Risk and Insurance. The authors thank Institute of Risk and Insurance for the support in data access. We would also like to acknowledge the editor. The work was supported by Beijing Social Science Foundation (14JDJGC011).
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Conflict of interest
The authors declare that they have no conflict of interest.
- 1.Hong et al (2016) New light rail transit and active travel: a longitudinal study. Transp Res Part A 92:131–144Google Scholar
- 4.Downing TE (2000) Towards a vulnerability science? [J/OL]. IHDP Newsletter Update, 3Google Scholar
- 5.Yuan P, Song S et al (2015) Study on the vulnerability evaluation of components of urban rail transit system. China Saf Sci Technol 07:142–149Google Scholar
- 6.Erath A, Birdsall J, Axhausen KW, Hajdin R (2009) Vulnerability assessment of the Swiss road network. 88th Transportation research board annual meeting, Washington, pp 1–17Google Scholar
- 8.Fang LB, Cai JD (2011) Reliability assessment of microgrid using sequential Monte Carlo simulation. J Electron Sci Technol 2011:31–34Google Scholar
- 13.Jenelius E, Mattsson Lars-Gran. Developing a methodology for road network vulnerability analysis. 12th–13th Nectar cluster 1 seminar, Molde University College, MoldeGoogle Scholar
- 16.Demichela M (2013) Vulnerability assessment for human targets due to ash fallout from Mt. Etna. Chem Eng 32:445–450Google Scholar
- 17.Yuan P, Song S et al (2014) Fire prediction based on combinational optimization model of gray neural network. China Saf Sci Technol 03:119–124Google Scholar
- 18.Yuan P, Song S et al (2015) Study on the cusp catastrophe of employees’ unsafe behavior. J Saf Environ 03:165–169Google Scholar
- 19.Yuan P, Song S et al (2014) Urban vulnerability modeling and simulation under climate change. Urb Dev Res 01:54–60Google Scholar
- 24.Song S et al (2016) Study on the influencing factors of electric fire in metro based on vulnerability theory. J Xi’an Univ Sci Technol 05:691–696Google Scholar