# Operational Risk Assessment of Engineering Vehicles Considering Driver Characteristics
## 5 Discussion  
The risks of engineering vehicle operation are closely related to human beings, vehicles, roads, environment, and management. However, reasonable safety improvement measures and countermeasures can effectively improve the safety of engineering vehicle operations, reduce the occurrence of accidents, and lower the severity of accidents. Aiming at the problems of frequent traffic accidents and the defective safety management of engineering vehicles, the risk assessment method is used to identify the operational risk elements of engineering vehicles. Moreover, a BP neural network model of operational risk assessment for engineering vehicles is established based on this method in order to realize the risk assessment of engineering vehicles. This study illustrates its approach by utilizing the vehicle dynamic supervision system data from Guangdong Province, employing the model, and delineating safety management strategies and countermeasures for engineering vehicles aimed at enhancing the safety of their operations. In this study, various risk factor indicators of 10 engineering vehicles in Shenzhen City taken from May to June 2023 are selected and combined with the formulated risk index judging standard for risk index assignment. The risk factors related to the 10 selected engineering vehicles are organized and assigned a value in the quantitative standard of risk index. The final input data are shown in Table 7.  
The network output results of the engineering vehicle operation risk assessment are derived using the trained BP neural network to simulate the operation of engineering vehicles. Moreover, the degree level is determined according to the output results. In particular, the output scores of 0-0.25, 0.25-0.5, 0.5-0.75, and 0.75-1 indicate the serious, \begin{table}
\begin{tabular}{c c} \hline \hline
**Indictors** & **BP Neural Network Model** \\ \hline MAPE & 11.835 \\ RMSE & 0.057 \\ R\({}^{2}\) & 0.912 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Calculation of evaluation indicators. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**No.**} & **Driver’s** & **Undesirable** & **Traveling** & **Segregation** \\  & **Driving** & **of Moorized** & **Position** \\  & **Behavior** & **Speed** & **Hours** \\ \hline
1 & 9 & 9 & 10 & 9 & 8 \\
2 & 8 & 7 & 6 & 7 & 8 \\
4 & 1 & 1 & 2 & 2 & 3 & 2 \\
5 & 5 & 7 & 9 & 4 & 7 & 9 \\
7 & 8 & 9 & 9 & 7 & 9 & 6 \\
8 & 9 & 4 & 1 & 8 & 9 & 10 \\
9 & 8 & 3 & 6 & 4 & 6 & 9 \\
10 & 7 & 5 & 3 & 2 & 5 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Input data statistics. general, basically safe, and safe degree levels, respectively. The risk assessment results of the operation of 10 engineering vehicles are shown in Table 8.  
Table 8 indicates that most of the 10 engineering vehicle operation risks are at the level of basic safety and above, which are acceptable risks under certain conditions. A few of them are at the level of serious and general. Therefore, measures must be taken so as to reduce the risk of the vehicle being driven. The above risk assessment results show that engineering vehicle operation risks involve drivers, road environment, company management, and other factors. Through the further investigation and analysis of relevant enterprises with average and serious evaluation results, it has been identified that their driver management systems, hazardous driving route condition management systems, and enterprise safety responsibility management systems exhibit varying degrees of deficiencies. Consequently, by assessing the feasibility of implementing pertinent safety management systems and methods (such as establishing a GPS monitoring system, conducting safety training for drivers and personnel in charge, etc. ), this study proposes the following safety improvement strategies from the perspectives of human factors, road environment factors, and management factors. Human Factor Strategies  
These strategies involve training and education for inexperienced drivers and those with poor driving behavior.