# Route Risk Index for Autonomous Trucks
## 5 Discussion  
The development of the composite risk index presented in this paper supports the need for a multifaceted assessment of risks associated with the deployment of autonomous trucking across the U.S. interstate system. While previous studies have examined transportation risks in isolation, this work synthesizes these risks into a singular, actionable index. This work also highlighted the critical importance of regional variations in risk factors, a dimension often overlooked in existing models. The analysis of the risk index, as visualized through the series of figures in the results section, reveals a complex tapestry of regional risk factors that influence the safety and efficiency of autonomous freight transportation. By integrating GIS methodologies with national traffic and safety databases, the constructed model captures the multifaceted nature of transportation risks. Using a quantitative approach offers a replicable and scalable model for risk assessment, capable of processing complex datasets to yield precise, actionable insights. The quantitative analysis enables a rigorous, data-driven exploration of risks, distinguishing it from more subjective approaches and emphasizing its value in strategic decision making. Identifying key drivers of risk in different regions contributes a more targeted approach for informed decisions in route planning, infrastructure development, and policy formulation. Figure 15: Dominant risk factors by state. General observations are that precipitation emerges as a significant risk factor across most regions, aligning with the pre-existing understanding that weather conditions are a primary contributor to transportation risks. To reduce regional bias, the analysis did not make a distinction between different types of precipitation such as rain versus snow and ice. This is based on the notion that any type of weather condition will affect the accuracy of sensors such as cameras, which can impede operational performance. The findings show how factors such as road roughness, traffic levels, precipitation, and fatality rates contribute differentially across regions. For example, in the Southeast and New England regions, precipitation emerged as a dominant risk factor, whereas, in the Far West, traffic volume and road roughness played more substantial roles. This distinction is crucial, as it highlights the regional specificity of risk profiles and challenges the notion of a one-size-fits-all approach to transportation risk management. Furthermore, the distinction between risk factors across regions emphasizes the need for customized risk management strategies, moving beyond a generic approach to address the specific challenges and requirements of each region. The implications of these findings are vast. For policymakers, this research provides a data-driven foundation for regional infrastructure planning and AV legislation. For the logistics industry, it offers a strategic guide for long-term route planning and risk mitigation that can enhance the safety and reliability of autonomous trucking operations. Furthermore, this work advances the body of knowledge by providing a methodological framework that can be adapted and applied to other areas of transportation risk assessment and infrastructure management. Despite its strengths, this research has some limitations. The risk index developed relies on the availability and accuracy of data from national databases, which may not capture real-time changes or the variability in local road conditions and traffic patterns. Furthermore, the normalization of risk factors to a uniform scale, while useful for comparison, may oversimplify the complexity of individual factors. The uncorrelatedness of factors may not capture non-linear effects among them, requiring a more complex evaluation such as employing empirical orthogonal function (EOF) analysis.