# Two-Component Bayesian Hierarchical Models for Cost-Benefit Analysis of Traffic Barrier Crash [PERSON]
###### Abstract  
Road departure crashes tend to be hazardous, especially in rural areas like Wyoming, Traffic barriers could be installed to mitigate the severity of those crashes. However, the severity of traffic barriers crashes still persists. Besides various drivers and environmental characteristics, the roadways and barrier geometric characteristics play a critical role in the severity of barrier crashes. The Wyoming department of transportation (WYDOT) has initiated a project to identify and optimize the heights of those barriers that are below the design standard, while prioritizing them based on the monetary benefit. This is to optimize first barriers that need an immediate attention, considering the limited budget, and then all other barriers being under design. In order to account for both aspects of frequency and severity of crashes, equivalent property damage only (EPDO) was considered. The data of this type besides having an over-dispersion, exhibits excess amounts of zeroes. Thus, a two-component model was employed to provide a flexible way of addressing this problem. Beside this technique, one-component hierarchical modeling approach was considered for a comparison purpose. This paper presents an empirical cost-benefit analysis based on Bayesian hierarchical machine learning techniques. After identifying the best model in terms of the performance, deviance information criterion (DIC), the results were converted into an equation, and the equation was used for a purpose of machine learning technique. An automated method generated cost based on barriers' current conditions, and then based on optimized barrier heights. The empirical analysis showed that cost-sensitive modeling and machine learning technique deployment could be used as an effective way for cost-benefit analysis. That could be achieved through measuring the associated costs of barriers' enhancements, added benefits over years and consequently, barrier prioritization due to lack of available budget. A comprehensive discussion across the two-component models, zero-inflated and hurdle, is included in the manuscript. Bayesian hierarchical; machine learning; zero inflated model; hurdle model; cost-benefit analysis +
Footnote †: journal:  
doi:10.3390/a1308017990  
while override could be expected for the short barriers. In Wyoming, there is a significant number of old barriers which do not satisfy current height standards. Upgrading these barriers to the appropriate height will require much resources over several years. It is therefore essential to develop a process for optimizing and upgrading barriers. Thus, the Wyoming department of transportation (WYDOT) has initiated a project to measure all the barriers' geometric characteristics in the state. More than a million linear feet of barriers geometric and roadside characteristics were collected and measured. The collected data would not only help to enhance the below-design barriers, but also to identify the relationship between crash severity and the barriers geometric characteristics through conducting a statistical analysis. In addition, due to limited budget, the project needs to identify which barriers' upgrades would result in higher benefits. The developed prioritization process will facilitate the gradual upgrade of barriers based on available annual budgets. In order to provide a prioritization process, monetary evaluation of barrier enhancement was conducted with a help of machine learning technique. The technique was used to inform the policy makers about how much money they could expect to gain after optimizing barriers. To fulfill the aforementioned points, several steps are worthy of mention, as follows:  
1. Both state highway and interstate systems were aggregated in this study. However, to account for variation in response across these two-highway systems, a dummy variable of highway type was incorporated in the analysis. 2. Various barriers were considered in this study.