Data Needs Analysis for Resilient Multimodal Rural Freight Corridors

Local and global trade heavily rely on Freight transportation corridors, acting as vital arteries supporting industrial activities and economic development. Productivity and efficiency of rural freight corridors are susceptible to factors such as natural disasters and manmade events. State Departments of Transportation are prioritizing national recovery plans to enhance the resiliency of freight corridors. This research aims to gather real-time, accurate data on rural industries and freight networks, evaluating resiliency using deep learning methodologies in Idaho as a case study. This multifaceted research initiative aims to fortify rural freight networks in Idaho across four stages. The initial phase involves an extensive review of advanced strategies for resilience, addressing both natural and man-made disasters' impact on Idaho's critical sectors. Subsequently, stakeholder interviews target data needs and rectify existing gaps, focusing on technological and coverage deficiencies. Practical application comes in the form of case studies, utilizing a grid-based methodology to analyze vulnerabilities in selected counties' freight networks. The culmination involves harnessing Artificial Intelligence to develop a deep learning approach, predicting resilience indices and redundancy. The ultimate aim is seamless integration into the Idaho Transportation Department's freight management system for robust decision-making support in the face of industry challenges. This project seeks to develop tools for decision-makers to identify and prioritize vulnerable segments in passenger and freight networks in Idaho. The resulting data can be utilized by the Idaho Transportation Department to pinpoint vulnerable links and assess freight roadway pathways, enhancing strategic planning and decision-making for improved network resilience.