# Risk Assessment of Fixed Obstacles along Roads
## 4 Defining Hypothesis  
There are a number of scientific disciplines that base the knowledge of the studied objects on the analysis of measured data. These are, for example, all statistical methods within frequency or Bayesian statistics. The classical methods are mainly concerned with static data analysis. It is the principle of Bayesian statistics that will be used in this article. This method is based on a dynamic model of a process and follows its evolution in time [21]. The property of variability may be inherent in a given variable, i.e., there are certain undetectable disturbances that affect the values, or this variability may be due to a certain relationship with another variable or several variables. This situation is described by the so-called correlation, which is also analyzed within the defined hypothesis [22]. The basic assumption for the correct functionality of the model is a monitored set of measured data with complete records. For each accident event recorded and used, all data must be complete for the variables represented in the model. At the same time, these data can be supplemented with expert assumptions that describe the behavior of the model. The following Table 1 shows the hypothesis in the form of an assumed (estimated) level of risk of fixed obstacles determined by the authors of the work and traffic safety experts. The values in the table are based primarily on the author's deep empirical understanding of the discussed issues, on consultations with experts in the practice, and last but not least on the above-mentioned related literature. In addition to the three basic levels of risk based on the methodology of safety inspections, the table is supplemented with an additional parameter--\"NO RISK\"--which indicates only material damage, not injury to the accident participant. The individual columns in the table specify the assumed severity hypothesis for individual fixed-obstacle exposures at the location of rural area sections of Class I roads. The first column represents the identification number of the specific combination and the second column defines the height of the fixed obstacle above the ground level. The third column discusses how exposure is affected by the impact of the road ditch. The fourth column describes the effect of the spatial guidance of the road, and the last column defines the distance of the fixed obstacle. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Combination**} & \multirow{2}{*}{
\begin{tabular}{c} **Heigh above** \\ **Ground Level** \\ \end{tabular} } & **Impact of the** & **Spatial Guidance** & \multicolumn{3}{c}{**Distance of Fixed Obstacle**} \\ \cline{3-7}  & & **Road Ditch** & **of the Road** & **(0–3 m-)** & **(3–6 m-)** & **(6–9 m-)** \\ \hline \multirow{2}{*}{\(1,19,37\)} & \multirow{2}{*}{\((-\infty\)–0.0 m)} & doesn’t slow & straight or slight & \multirow{2}{*}{High Risk} & High Risk & Medium Risk \\  & & & down & directional curve & & & \\ \hline \multirow{2}{*}{\(2,20,38\)} & \multirow{2}{*}{\((-\infty\)–0.0 m)} & doesn’t slow & other combinations of & \multirow{2}{*}{High Risk} & High Risk & High Risk \\  & & & down & spatial guidance & & & \\ \hline \multirow{2}{*}{\(3,21,39\)} & \multirow{2}{*}{\((-\infty\)–0.0 m)} & partially slows & straight or slight & \multirow{2}{*}{High Risk} & Medium Risk & Low Risk \\  & & & down & directional curve & & & \\ \hline \multirow{2}{*}{\(4,22,40\)} & \multirow{2}{*}{\((-\infty\)–0.0 m)} & partially slows & other combinations of & \multirow{2}{*}{High Risk} & High Risk & Medium Risk \\  & & & down & spatial guidance & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Assumed severity hypothesis for individual fixed-obstacle exposures at the location of rural area sections of Class I roads. The hypothesis defined is to verify whether the predicted severity of the fixed obstacles for each combination of the observed parameters listed in Table 1 corresponds to the actual level of risk for the crew of passenger vehicles.