# Two-Component Bayesian Hierarchical Models for Cost-Benefit Analysis of Traffic Barrier Crash [PERSON]
## 8 Data  
Three sources of datasets were aggregated for the final analysis. These data sources included: traffic, crashes, and barrier geometric characteristics. The crash and traffic datasets for this study originated from WYDOT. The crash data was filtered to incorporate crashes between 2007 and 2017. The traffic dataset included average annual daily traffic (AADT) and average annual truck traffic (ADTT). The response of the crash data was crash severity which was converted into EPDO based on the criteria highlighted by WYODOT (see Equation (16)). The second dataset, barrier geometric, included various roadway and barrier geometric characteristics including: barriers types, lengths, offsets, heights, and various roadside geometric characteristics, such as shoulder width. This dataset also included the starting and ending mileposts of traffic barriers and roadway IDs. The crash data was separated into interstate and state highways systems. The data included only those crashes involving hitting a barrier as their first harmful event were included in the data set. The crash dataset was aggregated across various barriers based on the milepost, direction of travel, and highway system. Due to low traffic on the state highway system, many traffic barriers have not been involved in crashes over the ten-year analysis period. As a result, a significant portion of these barriers on the state highway system are not within a recommended height. Thus, due to randomness of crashes and the fact that it is likely for these barriers to experience crashes, those barriers were added to the dataset resulting in a significant number of zero responses. It should be noted that all the risky zero crash barriers and barriers that experienced at least a crash were used for training the model. Then, the trained model was implemented on those barriers that were not within the range of barrier designs. Thus, the information is presented in Table 1 for the training data and Table 2 for the implementation of a trained model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable ID** & **Variable Description** & **Mean** & **Variance** & **Min** & **Max** \\ \hline \multicolumn{5}{c}{N = 2432} & \multicolumn{5}{c}{_All barriers_} \\ \hline Box beam as reference, concrete as 2 and W-beam as 3 & 1.7 & 0.906 & 1 & 3 \\ \hline Response (EPDO) & 7.6 & 1120.239 & 0 & 555 \\ \hline b1 & AADT & 2306 & 2,744,863.728 & 27 & 8853 \\ \hline b10 & Restrain condition, having some form of restrain as reference (0), 1 otherwise & 0.1 & 0.074 & 0 & 1 \\ \hline b12 & Lighting conditions, light condition as reference (0), 1 otherwise & 0.4 & 0.171 & 0 & 1 \\ \hline b2 & Alcohol involvement, no alcohol involvement as reference (0), 1 otherwise & 0.1 & 0.054 & 0 & 1 \\ \hline b3 & Barrier height, continuous & 29.6 & 9.652 & \textless{}20 & 56 \\ \hline b4 & Shoulder width, continuous & 5.6 & 12.444 & 0 & 58 \\ \hline b5 & Weather condition, clear weather condition as reference (0), 1 otherwise & 0.5 & 0.179 & 0 & 1 \\ \hline b6 & Roadway classification, state highway system as 0, 1 otherwise & 0.5 & 0.248 & 0 & 1 \\ \hline b7 & Gender, males as reference (0), 1 otherwise & 0.3 & 0.156 & 0 & 2 \\ \hline b8 & Length, continuous & 683 & 1.805 & 14 & 35,470 \\ \hline B17 & Highway classification & 0.4 & 0.248 & 0 & 1 \\ \hline b16 & ADTT & 483 & 208,279.501 & 2 & 2227 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary statistics of important predictors: the whole data being used for training.