## 2 Materials and Methods
### Model Development  
Considering the large number of crash cases and explanatory variables in the dataset, evaluation methods that combine statistical models and ML have better adaptability. The  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**I. Descriptive Statistics for Continuous Variables** & & & & & & \\
**Variable** & **Mean** & **S.D.** & **Variable** & **Mean** & **S.D.** \\ \hline \(\Delta\)V (km/h) & 26.25 & 21.01 & Mc (kg) & 995.80 & 444.37 \\ \hline EES (km/h) & 20.93 & 23.05 & Mass (kg) & 2831.23 & 3299.98 \\ \hline Vr (km/h) & 52.71 & 33.24 & Speed (km/h) & 55.19 & 32.96 \\ \hline CMI & 0.49 & 0.16 & Speed limit (km/h) & 72.96 & 22.38 \\ \hline CSI & 0.40 & 0.23 & Age & 43.23 & 19.02 \\ \hline Ed (Joules) & \(4.10\times 10^{6}\) & \(1.57\times 10^{7}\) & Lane number & 2.85 & / \\ \hline
**II. Descriptive Statistics for Discrete Variables** & & & & & \\
**Variable** & **Subscript** & **Count** & **\%** & **Variable** & **Subscript** & **Count** & **\%** \\ \hline Vehicle type & Light-duty & 13,697 & 56.87 & & 0:00–6:00 & 2508 & 10.41 \\ Pick up/SUV & 6431 & 26.70 & & 6:00–12:00 & 6435 & 26.72 \\ Truck & 3856 & 16.01 & & 12:00–18:00 & 10,342 & 42.94 \\ Bus & 98 & 0.41 & & 18:00–24:00 & 4797 & 19.92 \\ \hline  & Turning left/right & 4562 & 18.94 & & Rear-end & 4672 & 19.40 \\  & Proceeding straight & 15,022 & 62.38 & & Head-on & 5596 & 23.24 \\  & Decelerating/stopping & 2700 & 11.21 & & Side impact & 13,814 & 57.36 \\ Behavior & Overtaking/merging & 559 & 2.32 & & & Front side & 15,096 & 62.68 \\ before crash & Negotiating a curve & 1947 & 8.08 & Impact & Back side & 3042 & 12.63 \\  & Intersecting & 4694 & 19.49 & position & Near side & 3367 & 13.98 \\  & Braking and steering & 1525 & 6.33 & & Far side & 2577 & 10.70 \\  & Skidding & 2557 & 10.62 & & & \\ \hline  & Traveling on the wrong & 7010 & 29.11 & & Straight & 20,648 & 85.74 \\ Violation & & & & & & \\  & Alcohol & 1231 & 5.11 & & & \\  & Speeding & 3159 & 13.12 & & Curve & 3434 & 14.26 \\ \hline  & Male & 14,158 & 58.79 & & & Level & 18,554 & 77.04 \\ Sex & & & & & & Uphill & 708 & 2.94 \\  & Female & 9924 & 41.21 & & Downhill & 1935 & 8.03 \\  & & & & & & Hillorest/sag & 1060 & 4.40 \\ \hline Road type & One-way & 384 & 1.59 & & & & \\  & Divided two-way & 8436 & 35.03 & & & No barrier & 15,405 & 63.97 \\  & Undivided two-way & 13,427 & 55.75 & & & Unprotected & 4365 & 18.12 \\  & Ramp & 241 & 1.00 & & Positive protected & 4312 & 17.90 \\ \hline  & Spring & 5141 & 21.35 & & First row & 17,486 & 72.61 \\ Season & Summer & 6008 & 24.95 & & Second row & 5249 & 21.80 \\  & Autumn & 6729 & 27.94 & & Others & 1347 & 5.59 \\  & Winter & 6204 & 25.76 & & & & \\ \hline Weather & Clear & 17,275 & 71.73 & Roadway & Dry & 19,658 & 81.63 \\  & Not clear & 6870 & 28.53 & surface & Wet & 3288 & 13.65 \\  & Severity & SEV1 & 10,309 & 42.81 & Traffic & Slippery & 1136 & 4.72 \\ \hline  & SEV1 & 10,309 & 42.81 & & No control & 15,641 & 64.95 \\  & SEV2 & 6654 & 27.63 & & Traffic sign & 5445 & 22.61 \\  & SEV3 & 7119 & 29.56 & & & Traffic device & 2996 & 12.44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of crash dataset.  
accuracy of injury assessment depends on several aspects, mainly including model selection, feature selection, and hyperparameter optimization. This section introduces detailed establishment of the evaluation models.