# Route Risk Index for Autonomous Trucks
## 4 Results
### Regional Risk Distribution  
Figure 12 displays a choropleth map of the United States categorized by a region risk index, which quantifies the risk level across different regions. The color-coded risk index value for each region is according to the legend. The map illustrates significant regional variations in risk, with the Southeastern states exhibiting the highest risk indices, while the Rocky Mountain states have lower risk indices. The central states display a moderate level of risk. Figure 13 is a stacked bar chart that breaks down the composition of the average risk indices for regions in the United States. Each bar corresponds to a region, and the segments within each bar represent the contribution of the four risk components as indicated in the legend. The length of each colored segment within the bars indicates the proportion of each risk component contributing to the region's overall average risk index. The sum of these segments equates to the total average risk index for the region. This visual representation facilitates a comparative analysis of how different risk factors contribute to the overall risk across regions. One observation is that precipitation, IRI, and AADT are significant contributors to risk in all regions. However, the proportion of risk attributed to these factors varies by region, reflecting regional differences in traffic patterns, infrastructure quality, weather conditions, and safety outcomes. Figure 14 provides further visualization of the relative strength of risk components across regions. The bar chart depicts the mean proportional contribution of various risk factors across different U.S. regions. The y-axis represents the mean proportional contribution, calculated as the average contribution of each risk factor to the total risk index for that region. For instance, in the Far West, AADT makes up the most significant portion of the risk index, while in the Southeast and New England regions, precipitation is the dominant risk factor. The chart provides a clear visual comparison of how the significance of different risk factors varies regionally. Figure 12: Risk index by region. Figure 15 is a choropleth map of the United States with each state color-coded to represent the dominant risk factor from the developed risk index. The map provides a clear visual representation of which risk factor is most significant in each state, based on the analysis. For instance, states colored in purple have precipitation as their dominant risk factor, while those in orange are most affected by IRI. Figure 14: Risk component proportions by region. Figure 13: Risk index makeup by region. The above maps and their complementary bar charts are useful tools for stakeholders in transportation and logistics to dissect the geographic distribution of risks and strategize accordingly. They facilitate an integrated analysis by illuminating how these regional factors coalesce to form a multifaceted risk landscape. The spatial visualization supports the case for regional prioritization in transportation planning and the development of infrastructure, especially within high-risk areas such as the Southeastern states. Targeting investments to mitigate specific risks could bolster the safety of autonomous trucking routes in those regions. Moreover, the map highlights a critical need for region-specific policy frameworks that adeptly address the varied nature of transportation risks. It highlights the intricate nexus of factors behind regional transportation risks to help stakeholders craft focused, data-driven strategies in transportation management. The regional disparities in risk contributions suggest a targeted allocation of resources. For instance, regions plagued by road roughness may require heftier infrastructural investments, while those where precipitation poses significant risk could benefit from the adoption of weather-adaptive logistics planning.