Identifying Critical Routes in a Road Network for Natural Hazard Resilience: A Case Study

Abstract: In the context of natural disaster preparedness, identifying the most critical routes in a road network
is essential for effective resource allocation and infrastructure resilience. This paper presents an
application of a methodology to identify the critical routes in a road network based on multiple
parameters. The methodology utilizes an adapted version of the non-dominated sorting algorithm.
This algorithm is used in various applications to rank data sets without introducing bias. The
parameters considered in this study include betweenness centrality, which is used as a measure of
network connectivity importance, average daily traffic, and susceptibility to failure during natural
disasters, in this case, earthquakes. By integrating these diverse parameters, the methodology
comprehensively assesses the important routes in a road network. The application of the nondominated sorting algorithm approach is demonstrated using a case study region in California. The
top 10 most critical routes in the road network of the case study region are identified with regard
to the selected parameters. The findings from the case study application underscore this approach's
significance in offering a robust ranking scheme that combines various critical factors, thereby
enhancing the decision-making process for policymakers and civil engineers. The study offers a
valuable tool for improving disaster preparedness and infrastructure planning