A cost and emission optimization framework for strategic intermodal freight transportation infrastructure development

The freight transportation sector is critical to the economic prosperity in the US, but it also constitutes a major source of carbon emissions. Although intermodal transportation has been shown to increase operating efficiency and reduce carbon emissions, research on infrastructural support for intermodal transportation is still insufficient. In this study, we establish a mixed integer programming model to jointly optimize strategic infrastructure development decisions and freight transportation decisions over a long horizon. Our model features a mixture of traditional single-mode facilities and hybrid hubs that facilitate rail-water transportation integration. A branch-and-cut decomposition algorithm is developed to solve large-scale problems. We collect real-world freight, infrastructure, and operations data to conduct computational studies on the model performance and algorithm efficiency. We provide insights for practitioners to address infrastructure planning and budgetary concerns. A case study using the established model at the national scale shows that well-optimized transportation infrastructure investment could have over 300% return during a 25-year horizon. In addition, fully capitalizing on the maturing clean vehicle technologies could reduce carbon emissions by 73.56 million tons at an annual rate.