# Deciphering Autonomous Vehicle Regulations with Machine Learning
## 3 Methodology
### Data and Feature Engineering
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Feature** & **Description** & **Source** \\ \hline \multirow{6}{*}{Gov.} & Votes\_D & Democratic electoral college votes reported in 2021 & [16] \\  & Votes\_R & Republican electoral college votes reported in 2021 & [16] \\  & VDPpPOPM & Democratic electoral college votes per million population & Derived \\  & VRPpPM & Republican electoral college votes per million population & Derived \\  & MPO & Metropolitan planning organizations (MPOs) & [17] \\  & POPMpMPO & Million population per MPO & Derived \\ \hline \multirow{6}{*}{Eco.} & GDP\_B & State gross domestic product (GDP) in billion dollars & [18] \\  & GDPpPOP & GDP\_B/POP\_M or GDP (USD B) per million population (POP) & Derived \\  & TM\_B & Billion non-fines (TM) & [19] \\  & TM\_WMT & TM\_B/VMT\_HW\_B or TM per vehicle miles traveled (VMT) & Derived \\  & Port\_M\_Ton & Million tons moved by seaports & [17] \\  & HW\_Rev\_M & State and local highway (HW) revenue (millions) & [20] \\  & REVPM & HW\_Rev\_M/TM\_B or USD M revenue per B ton-miles & Derived \\  & HW\_Exp\_M & State and local highway expenditures (millions) & [20] \\  & EXPpTM & HW\_Exp\_M/TM\_B or USD M expense per B ton-miles & Derived \\  & REVpEXP & HW\_Rev\_M/HW\_Exp\_M or highway revenue/expense ratio & Derived \\ \hline \multirow{6}{*}{Energy} & BTU\_T & Trillion British thermal units (BTU) consumed by transport & [21] \\  & BTU\_VMT & BTU\_T/VMT\_HW\_B or trillion BTU per billion VMT & Derived \\  & Pertol\_BBTU & Gasoline consumed by transport (billion BTUs) & [21] \\  & GxSpVM & Petrol\_BBTU/VMT\_HW\_B or BTUs of gasoline per VMT & Derived \\ \hline \multirow{2}{*}{Social} & POP\_M & State population (millions) & [22] \\  & POPPLA & POP\_M \(\times\) 1E6/LSqMi or population per sq mi of land & Derived \\ \hline \multirow{6}{*}{Env.} & SqMi & State area in square miles & [23] \\  & LSqMi & Land area in square miles & [23] \\  & WSqMi & Water area in square miles & [23] \\  & WISqMi & Inland water in square miles & [23] \\  & WCSqMi & Coastal water in square miles & [23] \\  & CO2\_MMT & Million metric tons of CO\({}_{2}\) emitted by transportation & [21] \\  & CO2 pPOP & CO2\_MMT/POP\_M or annual CO\({}_{2}\) ton per capita & Derived \\  & Snow\_Days & Annual days of snow on average & [24] \\  & Snow\_Ins & Annual inches of snow on average & [24] \\ \hline \multirow{6}{*}{Infra.} & Pub\_Road\_Mi\_K & Miles (thousands) of public roads & [17] \\  & ULMiles & Urban interstate miles & [17] \\  & RI\_Miles & Rural interstate miles & [17] \\  & RoadpLand & Pub\_Road\_Mi\_K per LSqMi & Derived \\  & UIPLand & ULMiles per LSqMi & Derived \\  & RI\_Handel & RI\_Miles per LSqMi & Derived \\  & PCT\_Sys\_Acc & Percentage of system (overall) acceptable IRI & [17] \\  & Bridges & Number of bridges in 2021 & [17] \\  & BRPPOPK & Bridges/(POP\_M \(\times\) 1000) or bridges per thousand population & Derived \\  & F\_Rail\_Mi & Freight rail miles & [17] \\  & RailpLand & Freight rail miles per LSqMi & \\  & PORTpPOP & Port\_M\_Ton/POP\_M or port tons per capita & Derived \\ \hline \end{tabular}
\end{table} 
Table 1: Features selected and engineered for ML. As shown in Figure 1, the feature engineering process of the methodological workflow (Figure 1) included variable proportioning, aggregate variable trimming, outlier removal, collinearity reduction, and variable standardization. Aggregate variables tend to have heavily skewed distributions because they reflect the large variation in a dataset, such as state area and population, leading to distorted model performance. Proportional variables reduce skewness and provide control for confounding factors. For instance, a larger population might naturally have a higher total GDP, whereas GDP per capita controls for population size, giving a clearer picture of economic health. That is, proportional variables normalize the scale of the data to make them more meaningful for comparison across districts.