A Granular Framework for Modeling the Capacity Loss and Recovery of Regional Transportation Networks under Seismic Hazards: A Case Study on the Port of Los Angeles

Abstract

Earthquakes, being both unpredictable and potentially destructive, pose great risks to critical infrastructure systems like transportation. It becomes crucial, therefore, to have both a fine-grained and holistic understanding of how the current state of a transportation system would fare during hypothetical hazard scenarios. This paper introduces a synthesis approach to assessing the impacts of earthquakes by coupling an image-based structure-and-site-specific bridge fragility generation methodology with regional-scale traffic simulations and economic loss prediction models. The proposed approach’s use of context-rich data such as OpenStreetMap and Google Street View enables incorporating information that is abstracted in standard loss analysis tools like HAZUS in order to construct nonlinear bridge models and corresponding fragility functions. The framework uses a semi-dynamic traffic assignment model run on a regional traffic network that includes all freeways and local roads (1,444,790 edges) and outputs traffic volume on roads before and after bridge closures due to an earthquake as well as impacts to individual trips (42,056,426 trips). The combination of these models enables granularity, facilitating a bottom-up approach to estimating costs incurred solely due to physical damage to the transportation network. As a case study, the proposed framework is applied to the road network surrounding the Port of Los Angeles---an infrastructure of crucial importance---for assessing resilience and losses at a high resolution. It is found that the port area is disproportionately impacted in the hypothetical earthquake scenario, and delays in bridge repair can lead to a 50% increase in costs.