Vulnerability modeling, assessment, and improvement in urban metro systems: A probabilistic system dynamics approach

The urban metro system is a complex system that is vulnerable to various kinds of hazards and subjected to dynamics, where errors in any part are very likely to cause system failures and accidents. A better understanding of evolutional dynamics of the system vulnerability over time enables to ensure the safety and security in its operation and provide guidance for long-term operation and maintenance for the metro system. However, there are limited studies considering the long-term evolutional dynamics and uncertainties of the vulnerability in urban metro system. To solve the concern, the objective of this research is to develop a hybrid approach that integrates system dynamics (SD) and Monte Carlo (MC) simulation to evaluate the vulnerability of the metro system in operation from a long-term perspective. By merging multi-resource information, an SD model consisting of 4 sub-systems (i.e., workforce vulnerability, organizational vulnerability, equipment vulnerability, and environmental vulnerability) and 16 feedback loops is constructed to reveal complex relationships among various factors and simulate the overall system vulnerability. The MC simulation is used to analyze the sensitive factors and improvement strategies of vulnerability in urban metro systems under uncertainty. The operation network of the Wuhan metro system in China is utilized as a case study to verify the applicability of the proposed approach. Results indicate that (i) It is predicted that the overall system vulnerability will decrease at the early stage of operation and rise at the later stage with the rapid decline of equipment system status; (ii) The workforce sub-system is discovered to be more sensitive to the overall metro system globally than the other three subsystems, with the 10% and 20% fluctuations in input factors' variations considered; (iii) The strategy that aims to improve the safety training and equipment maintenance and operation frequency is identified as the most effective strategy to reduce the vulnerability of the overall metro system. The developed approach can be used as a decision tool that is capable of modeling the long-term vulnerability of the overall metro system and discovering appropriate improvement strategies towards more sustainable transit development.

Automated summary

This research aims to develop a hybrid approach integrating system dynamics (SD) and Monte Carlo (MC) simulation to evaluate the long-term vulnerability of urban metro systems. By merging multiple resource information, an SD model with 4 sub-systems and 16 feedback loops is constructed to reveal complex relationships among factors and simulate overall vulnerability. The study utilizes MC simulation to analyze sensitive factors and improvement strategies of metro system vulnerability under uncertainty, with promising results for enhancing safety and security in metro operations.