# Route Risk Index for Autonomous Trucks
## 4 Results
### Risk Factor Distribution  
Table 1 summarizes the overall statistics of the risk factors for both the original and normalized values. The mean AADT was approximately 52,727 vehicles, with a standard deviation (STD) of 39,141, indicating a wide range of traffic levels across the road segments. The average IRI value was around 92.57, with a standard deviation of 31.74, suggesting variability in road surface conditions. The mean precipitation level was 38.43, with a 14.24 standard deviation, reflecting differing weather conditions across locations. On average, there were approximately 11 fatalities per road segment per state, but with a high standard deviation of 19, indicating significant variability in safety outcomes. Figure 4 shows box plots of the distribution of the four risk components by region. For reference, Figure 5 shows the U.S. regional definitions according to the U.S. Bureau of Economic Analysis, which mirror current AT testing. The box plots provide a visualization of the central tendency, dispersion, and skewness of the data distribution, highlighting outliers as diamond shaped points. The edges of the colored boxes represent the interquartile range (IQR) from Q1 to Q3, representing the middle 50% of the data. The line within the box represents the median value. The whiskers above and below the boxes represent the maximum and minimum values, respectively, excluding outliers. The individual dots above the whiskers represent outlier values, defined as 1.5 times the IQR. AADT: The Far West region exhibits the widest IQR, suggesting a high variability in AADT. The Rocky Mountain region shows a comparatively low median and small IQR, which could indicate more uniform traffic patterns with fewer extreme values. IRI: The median IRI values across the regions do not vary significantly; however, the Southeast, Far West, and Mideast regions have a larger spread of IRI scores, indicating a wide range of road quality. Outliers are present in several regions, most notably in the Southeast, which could be indicative of particularly rough patches of interstate. Fatalities: The Southwest stands out with the highest median value for fatalities and several outliers, which might be a point of concern for transportation safety in that region. The New England, Mideast, Great Lakes, and Plains regions have notably lower median values and fewer outliers, suggesting fewer fatalities on their interstates. Precipitation: The Southeast has the highest median precipitation, which is consistent with its climatic patterns, as shown in Figure 3. The Far West has a significant number of outliers, showing that some areas receive much higher rainfall. The Rocky Mountain region shows lower precipitation levels, with a particularly tight IQR, indicating consistent weather patterns in terms of precipitation. These box plots provide valuable insights for AT deployment planning. For example, high variability in AADT could require dynamic planning for freight movement. High IRI values may necessitate more frequent maintenance schedules, and high fatality rates could lead to increased insurance costs and the need for enhanced safety measures. Finally, precipitation data could be crucial when planning routes to avoid weather-related delays. These results provide a quantitative foundation for policy recommendations and strategic planning. Figure 6 presents a pair plot with histograms and scatter plots, illustrating the relationships between the four normalized variables. The histograms on the diagonal show the distribution of each variable, while the scatter plots show the relationships between pairs of variables. The scatter plots in the upper right triangle of the grid annotate the Spearman correlation coefficient and its corresponding \(p\)-value. Similarly, the lower left triangle of the grid annotates the Kendall correlation coefficient and its corresponding \(p\)-value.