[Paper Review] Evaluating resilience in urban transportation systems for sustainability: A systems-based Bayesian network model

Junqing Tang, Hans Rudolf Heinimann+3 more|arXiv (Cornell University)|Aug 26, 2019

Infrastructure Resilience and Vulnerability Analysis25 references4 citations

TL;DR

This study proposes a systems-based Bayesian network model to quantitatively evaluate long-term, multi-dimensional resilience in urban transportation systems across four Chinese cities (1998–2017). The model integrates design, construction, operation, and innovation factors, revealing moderate overall resilience (50–60%) with non-linear dynamics—notably a 'V'-shaped trend in Beijing and Tianjin—highlighting the critical role of adaptive rebuilding and change capabilities for future resilience.

ABSTRACT

This paper proposes a hierarchical Bayesian network model (BNM) to quantitatively evaluate the resilience of urban transportation infrastructure. Based on systemic thinkings and sustainability perspectives, we investigate the long-term resilience of the road transportation systems in four cities of China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The model takes into account the factors involved in stages of design, construction, operation, management, and innovation of urban road transportation, which collected from multi-source data platforms. We test the model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience of all four cities' transportation infrastructure is within a moderate range with values between 50% to 60%. Although they all have an ever-increasing economic level, Beijing and Tianjin demonstrate a clear "V" shape in the long-term transportation resilience, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience-economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that urban decision-makers should pay more attention to the capabilities of quick rebuilding and making changes to cope with future disturbance. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building sustainable infrastructure.

Motivation & Objective

• To develop a holistic, systems-based framework for evaluating long-term, multi-dimensional resilience in urban transportation infrastructure.
• To examine the coupling relationship between transportation resilience and regional economic development over two decades (1998–2017).
• To identify key drivers of resilience through sensitivity and backward inference, supporting proactive decision-making.
• To provide a decision-support tool for urban planners and policymakers focused on sustainable infrastructure development.

Proposed method

• A hierarchical Bayesian network model (BNM) is constructed using systemic thinking and sustainability principles to represent resilience across design, construction, operation, management, and innovation stages.
• Conditional probability tables (CPTs) are populated using expert knowledge and qualitative assessments to handle uncertainty in resilience evaluation.
• The model employs forward inference to predict overall resilience, sensitivity analysis to identify influential factors, and backward (abductive) inference to diagnose root causes of low resilience.
• Multi-source data from public archives are used to inform the model structure and validate resilience trends across Beijing, Tianjin, Shanghai, and Chongqing.
• The model explicitly captures non-linear, dynamic, and multi-dimensional resilience characteristics, distinguishing it from traditional network-based robustness measures.
• Sensitivity and abductive reasoning are used to identify critical resilience drivers, particularly rapid rebuilding and adaptive change capabilities.

Experimental results

Research questions

• RQ1 How does long-term transportation resilience evolve in major Chinese cities from 1998 to 2017, and what patterns emerge in relation to economic development?
• RQ2 What are the key system-level factors that most significantly influence the resilience of urban transportation infrastructure?
• RQ3 To what extent is transportation resilience correlated with regional economic growth, and does this relationship exhibit non-linear dynamics?
• RQ4 How can Bayesian networks support backward diagnosis and proactive decision-making in resilience planning for urban infrastructure?

Key findings

• The overall resilience of urban transportation systems in Beijing, Tianjin, Shanghai, and Chongqing ranges between 50% and 60%, indicating a moderate level of resilience over the 1998–2017 period.
• Beijing and Tianjin exhibit a distinct 'V'-shaped resilience trend, indicating a non-linear, dynamic coupling effect between resilience and economic development.
• Resilience does not consistently increase with economic growth, revealing a complex, multi-dimensional relationship that defies simple correlation.
• Sensitivity and backward inference identify 'quick rebuilding' and 'adaptive change' capabilities as the most influential factors in enhancing system resilience.
• The Bayesian network model successfully captures non-linear, multi-dimensional resilience dynamics, offering a robust tool for diagnostic and predictive analysis.
• The model’s abductive inference capability enables effective problem detection and supports targeted interventions in infrastructure planning and management.