# Route Risk Index for Autonomous Trucks
## 3 Methodology
### Analytical Workflow  
Figure 1 illustrates the data synthesis workflow consisting of individual procedures that are either coded in software or implemented using a GIS tool. The \"GIS Extract\" procedure of the workflow kept only those linear objects representing interstate routes, along with their AADT and IRI features, by setting a filter for F_SYSTEM = 1. For example, Figure 2 shows the result of extracting the linear GIS objects representing the two interstate routes (red lines) in North Dakota. The blue lines represent other roadway types. The workflow then iteratively joined the extracted interstate shapefiles until it processed the shapefile layers for all states of the contiguous United States (CONUS). Figure 1: The data synthesis workflow. Figure 2: Extraction of the linear GIS objects representing the two interstates in North Dakota. The spatial joining of the precipitation polygons of U.S. counties averaged the mean annual precipitation across intersecting road segments. Figure 3 depicts the mean annual precipitation for CONUS counties from 1901 to 2000. This illustration helps to interpret the results of where risk factors dominate. In processing the FARS data, the workflow kept only the data for crashes on the inter-state routes. The workflow then spatially joined those to the previously joined shapefiles containing the AADT, IRI, and precipitation values for each interstate segment. Due to the fragmented regulatory constraints among states, autonomous trucking companies often make deployment decisions at the state level [28]. Therefore, the workflow aggregated the risk index for an entire interstate section that falls within individual states. In preparation for merging aggregated values at the state and road levels, the GIS procedure created a merge key containing a string of the state abbreviation and the interstate route number. The data aggregation that followed computed the mean values for AADT, IRI, and precipitation and the sum of fatalities along the entire interstate within a state. Performing the aggregation on an extracted data table was more convenient due to the enhanced programming functions available outside of the GIS environment. Specifically, the authors used Python along with programming libraries such as pandas, seabom, and scipy to plot descriptive statistics of each factor such as bar charts and box plots to visualize the risk factors across states and regions. The next strategy was to normalize the four factors within the [0, 1] range so that they became more comparable in a unitless dimension. The normalization is as follows:  
\[N(x)=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{1}\]  
This study defines the composite risk index (\(CRI\)) as a uniformly weighted linear combination of the four normalized factors as follows:  
\[CRI=\frac{w_{1}\cdot N(TV)+w_{2}\cdot N(RC)+w_{3}\cdot N(SR)+w_{4}\cdot N(WP)}{ \sum_{i=1}^{4}w_{i}} \tag{2}\]  
where the following are true:  
* \(TV\) represents the traffic volume. Figure 3: Mean annual precipitation for CONUS counties from 1901 to 2000. \(RC\) denotes the road condition in terms of roughness. * \(SR\) stands for safety records such as fatalities. * \(WP\) indicates weather patterns, with mean annual precipitation in this context. * \(N()\) is the normalizing function in Equation (1). * \(w_{1}\), \(w_{2}\), \(w_{3}\), and \(w_{4}\) are weights assigned to each risk factor, reflecting their relative importance. The summation in the denominator ensures that the \(CRI\) remains normalized and comparable. This baseline research sets all the weights to unity because there is currently no other information about their relative importance in the overall risk, which future work will explore. This definition ensures a uniform approach to risk assessment across all states and routes. The results section further justifies this definition by showing that, statistically, the factors lack correlation.