# Operational Risk Assessment of Engineering Vehicles Considering Driver Characteristics
## 6 Conclusions and Future Work  
This study is based on respondents located in Shenzhen on the central coast of the southern Chinese province of Guangdong. Based on the drivers' driving characteristics, factors including driver characteristics, road environment, and management were selected in this study to evaluate the operational risks of engineering vehicles using the error BP algorithm. The optimal model was determined. The conclusions obtained are as follows:  
1. The driving skill scores of the drivers of engineering vehicles, buses, and other vehicles showed no significant difference. However, the main differences were in speed control, driving behavior, emotional control, and driving distraction. Engineering vehicle drivers performed best in terms of speed control, driving behavior, and emotional control;
2. Four risk levels were identified. Four factors, such as driver's driving experience, undesirable driving behavior, traveling speed, and continuous driving hours, were selected as driver's factors. Four factors, including segregation of motorized and nonmotorized lanes, road congestion index, and weather conditions, were also selected as road environmental factors. Moreover, three indexes, such as accidents at transportation companies, number of times the companies were notified, and GPS offline rate of engineering vehicles, were selected as factors for management;
3. Comparative analysis revealed that employing 64 neurons in the hidden layer yielded results closer to the actual value compared to utilizing 32 neurons. Additionally, the examination of various activation functions indicated the superiority of the sigmoid function in minimizing loss values. Nonetheless, overfitting phenomena were observed, necessitating the adoption of regularization optimization techniques. Furthermore, the model's convergence was enhanced following L2 regularization implementation;
4. The factors discussed in this paper are limited, and the sample size of the discussion is also limited. We hope that we can investigate data from more cities to further expand the scope of the study and refine the sampling methodology, as more variables are worth discussing to derive more in-depth analysis. For example, factors of the environment, vehicle emissions and pollution can be discussed. We hope that more factors will be added in future works to make the model more comprehensive.  
Conception, S.Q. and A.Z.; data collection, S.Q. and A.Z.; formal analysis, J.T. and S.Q.; software, S.Q. and A.Z.; methodology, S.Q. and A.Z.; funding acquisition, X.Z. and J.T.; writing--original draft, S.Q., X.Z. and A.Z.; writing--revised draft, S.Q., J.T., X.Z. and A.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52102410), Guangxi Youth Science Foundation (Grant No. 2021 GXNSFBA075022), 2022 Guangdong Social Science Planning Project (Grant No. GD22 YGL18).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
This research was support of the postdoctoral project of Shenzhen Technology Institute of Urban Public Safety. We also thank [PERSON] from State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, [PERSON] from Shenzhen Technology Institute of Urban Public Safety, as they were very helpful in rewriting and improving the article.  
The authors declare no conflicts of interest.