Using machine learning to examine freight network spatial vulnerabilities to disasters: A new take on partial dependence plots. (June 2022)

• Record Type: Journal Article
• Authors: Johnson, Paul M.; Barbour, William; Camp, Janey V.; Baroud, Hiba
• Is Part Of: Transportation research interdisciplinary perspectives. Volume 14(2022)
• Publication Date: 2022-06
• Subjects: Network vulnerability -- Transportation disruption analysis -- Partial dependence plot -- Gradient boosting machine -- Freight simulation; Transportation -- Periodicals; 388.05
• DOI: 10.1016/j.trip.2022.100617
• Languages: English
• ISSNs: 2590-1982

Abstract

Abstract: Analyzing transportation network vulnerabilities to disruptions is crucial for society to maintain commodity flows across the globe. However, most vulnerability analyses focus on impacts that arise from the deterioration of single network components, which can overlook spatial correlations between multiple components that manifest during area-spanning disruptions, such as those stemming from natural hazards. Here, we demonstrate an intuitive, simulation-based approach for inferring spatial vulnerabilities to area-spanning disruptions. In particular, we show how partial dependence plots derived from gradient boosting machines trained on the results of a routing simulation can be used to depict the average effect a disruption's location has on impacts while controlling for other input variables and spatial dependencies embedded in the network. Although we demonstrate our approach for Middle Tennessee's intermodal road and rail freight transportation network, our framework can easily be applied to other networks. Highlights: PDPs can be an intuitive way to depict spatial vulnerabilities in networks. The PDPs are derived from surrogate models that predict simulated disruptions. We demonstrate our approach on Tennessee's intermodal road and rail network.