# Route Risk Index for Autonomous Trucks
## 4 Results
### Route Risk Distribution  
Figure 8 is a map displaying the U.S. Interstate Highway System, with routes color-coded according to the deviation from the mean of the risk index. This map helps with visualizing geographic variations in risk across the interstate network. The routes in  
Figure 7: Distribution of the risk index and results of the test for normality.  
blue have an average risk index that is less than one standard deviation below the mean, indicating lower-than-average risk. Conversely, the routes in red have an average risk index that is more than one standard deviation above the mean, indicating higher-than-average risk. The other two colors represent routes that have an average risk index within one standard deviation of the mean, representing slightly lower (green) and slightly higher (orange) than average risk, respectively. The color gradation provides a quick reference to identify areas that might require more in-depth analysis or increased safety measures for AT deployments.  
The map integrates the risk index with spatial analytics to inform decision-making processes for transportation planning and safety management. It will help stakeholders to visualize and prioritize resource allocation for infrastructure improvements, enforcement of safety regulations, and implementation of advanced technologies aimed at risk reduction. The concentration of higher-risk routes (indicated in red) in certain areas could be a result of multiple factors, such as high traffic volumes, poor road conditions, higher accident rates, or severe weather conditions. Conversely, routes with lower risk scores are associated with better infrastructure, less traffic, safer driving conditions, or milder weather.  
Figure 9 is a stacked bar chart that illustrates the composition of risk indices for various U.S. interstate segments, categorized by regions. The risk components include mean daily traffic, road roughness, fatalities, and precipitation. Each bar represents an interstate segment identified by its number and associated region (e.g., MD_495 in the Mideast region), with the total length of the bar corresponding to the composite risk index for that segment. The chart displays the interstate segments ordered by risk index, with the highest at the bottom. The grayscale color coding within each bar denotes the contribution of each risk component to the total risk index as noted in the \"Risk Components\" legend. This visualization allows for the comparison of risk profiles across different interstate segments. For example, one can observe that precipitation is a predominant risk factor for many of the interstate segments, especially those with the highest overall risk index. The regional labels provide additional context by showing the geographical distribution of these interstate segments. For instance, segments in the Southeast (e.g., FL_95, FL_395, LA_110) rank near the top risk indices, which could be related to various factors such as higher traffic volumes, road conditions, or regional driving behaviors.  
Figure 8: Risk index for interstate routes within each state.  
Figure 9 is essential for understanding the relative importance of each risk component within each interstate segment and can guide targeted interventions. For transportation planning, this figure emphasizes the need to address specific risk factors on certain routes, which could be beneficial for prioritizing infrastructure investments, policymaking, and risk mitigation strategies, especially in the context of enhancing safety for autonomous trucking operations. Stakeholders can reference this figure when evaluating the application of the risk index in identifying high-priority areas for AT deployment and the need for tailored interventions based on the specific risk profiles of interstate segments considered.