# Route Risk Index for Autonomous Trucks
## 6 Conclusions  
The strategic implementation of autonomous trucking hinges on an intricate understanding of the risks involved in navigating the diverse U.S. interstate landscape. This study addressed the critical need for a regionally sensitive risk assessment tool, complementing broad-brush approaches that do not consider the localized intricacies of risk factors influencing transportation safety and efficiency. This comprehensive analysis informed the creation of a composite risk index, meticulously constructed using GISs and corroboratedby data from established national databases. The findings illuminate the pronounced regional variations in risk factors, with AADT, IRI, fatalities, and precipitation contributing unevenly across different states. This study substantiated these findings by employing robust methods that synergize spatial analysis with statistical normalization to distill complex datasets into a coherent risk profile. The benefits of this contribution are manifold, particularly for entities involved in the integration of autonomous trucking networks, such as transportation agencies, policymakers, and logistics companies. The detailed maps of risk factors provided can help to inform decision making about where to focus enhanced road safety protocols, prioritize infrastructural investments, and select routes to reduce risk.  
This research provides a blueprint for adapting risk assessment methodologies to other domains within transportation and beyond, where state and regional factors significantly influence operational risks. The practical applications of this work range from informing national transportation safety guidelines to aiding in the design of AV navigation systems that can dynamically respond to the risk profiles of the routes they traverse. The ability to adapt and generalize this method to address different scales and scopes of risk assessment is central to its long-term value. This study's approach echoed broader themes such as the interplay between technology and infrastructure, and the importance of data-driven policy.  
Future work will focus on addressing the limitations discussed earlier, particularly the need for real-time data integration and the refinement of risk factor weighting within the index. Further research may also explore the application of machine learning techniques to predict and adapt to emerging risk patterns, thereby enhancing the resilience and reliability of autonomous trucking routes. The workflow presented ensures that future explorations into transportation risk assessment are both grounded in empirical evidence and attuned to regional variability.  
Conceptualization, R.B. and R.J.; methodology, R.B. and R.J.; software, R.B. and R.J.; validation, R.B., R.J. and D.T.; formal analysis, R.B. and R.J.; investigation, R.B. and R.J.; resources, D.T.; data curation, R.B. and R.J.; writing--original draft preparation, R.B. and R.J.; writing--review and editing, R.B., R.J. and D.T.; visualization, R.B. and R.J.; supervision, R.B. and D.T.; project administration, D.T.; funding acquisition, D.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the United States Department of Transportation, Federal Motor Carrier Safety Administration, grant number FM-MHP-23-001.  
Not applicable.  
The data presented in this study are openly available as cited.  
The authors declare no conflicts of interest.