A Bayesian network approach for assessing the general resilience of road transportation systems: A systems perspective

TL;DR

This paper develops a Bayesian Network Model to evaluate the overall resilience of Beijing's road transportation system over two decades, highlighting key influencing factors and showing resilience trends.

Contribution

It introduces a novel Bayesian network approach based on a function-oriented resilience framework and ontological interdependence among system qualities.

Findings

Resilience probability ranged from 50% to 70%, lowest in 2006.

Resilience increased steeply after 2006.

System capabilities like robustness and quick repair most influence resilience.

Abstract

We proposed a Bayesian Network Model (BNM) based on function-oriented resilience framework and ontological interdependence among 10 system qualities to probabilistically assess the general resilience of the road transportation system in Beijing from 1997 to 2016. We tested the model with multi-source data collected from various sectors. The system qualities were examined by analysis of sensitivity and influence. The result shows that the general resilience of Beijing's road system exhibits a "V" shape in its trend, with the probability of being generally resilient between 50% and 70%, and at its minimum in 2006. There was a steep increase in such a probability since 2006. In addition, the general resilience of Beijing's road transportation system is most affected by its capabilities: (1) to rebuild its performance, (2) to be robust, (3) to adapt, (4) to change, and (5) to quickly repair damaged parts. The proposed BNM is a promising tool for multi-dimensional and systematic analysis, instead of finding a one-size-fits-all quantification criterion for resilience.