Abstract
Objectives
The main objective of this paper is to review the resilience of multimodal freight networks under various disruptions, both natural and manmade. We seek to achieve this through a series of interconnected objectives: 1) Establishing a clear understanding of multimodal freight transportation network resilience by synthesizing diverse definitions from the literature; 2) exploring models employed in simulating multimodal freight network resilience, including emerging trends and best practices; 3) identifying indexes and metrics used for assessing resilience; and 4) categorizing and analyzing the types of disruptions studied in relation to multimodal freight transportation networks, from natural disasters to human-made acts.
Methods
A systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to rapidly review definitions, models, metrics, and indexes regarding the resilience of multimodal freight transport networks.
Results
We identified a total of 23 studies examining freight transportation network resilience. Among these, seven studies utilized topology-based models to analyze network resilience. One study employed a finite element simulation-based model to assess network behavior under various conditions, while eight studies performed mathematical optimization models to optimize network performance and resource allocation. Additionally, two studies conducted probabilistic models to evaluate the likelihood and impact of disruptions on freight networks, and another two studies implemented real-time analysis models to monitor and respond to changing network conditions in real time. Only one study has used an image-based model to analyze disruption impact on network infrastructure. Although there were few investigations based on advanced high-fidelity models, or real-time analysis models, these approaches were less common among the reviewed studies. Most of the studies found in the literature have been verified using real-world case studies, providing practical perceptions into network resilience. However, a limited number of studies have been validated or calibrated based on actual disruption scenarios, which highlights an important area for potential improvement and further research in future studies.
Conclusion
Freight transportation network resilience is a multifaceted concept encompassing characteristics such as redundancy, functionality, robustness, and vulnerability. The interactions between different modes within multimodal freight corridors enhance network efficiency and resilience, while advanced modeling techniques, such as image-based network flow, agent-based, and finite element simulation models, offer understandings into freight network behavior following a disruption. Optimization models help minimize efficiency losses, delays, and costs during disruptions. These approaches collectively enable freight networks to adapt and recover from unforeseen events, supporting global trade and economic development.