Research On The Risk Assessment And Management Optimization Method Of Public Transport Vehicle Based On Early Warning Data

In recent years,with the rapid development of China’s economy and society,traffic accidents caused by public transportation vehicles have become increasingly frequent.In order to reduce the number of bus accidents and improve the safety of bus operations,bus vehicles are equipped with an intelligent early warning system to dynamically monitor the operational safety status and effectively prevent traffic accidents.The method of using the existing vehicle early warning data is of practical significance for the scientific and systematic assessment of the operation risk of public transport vehicles and the reduction of casualties and property losses caused by traffic accidents.The main work and conclusions are as follows:1.This paper analyzes the development trend of public transport in China,the current situation of safety management and the application of vehicle supervision technology in public transport safety management,and analyzes from three aspects:public transport operation safety management,traffic safety evaluation and traffic safety accident prediction.It is pointed out that the reasonable use of early warning big data is of great significance to improve the level of public transport vehicle safety supervision.2.This paper studies the related factors of the safe operation of public transportation from the four aspects of human,vehicle,road and environment.It introduces the main functions,installation,system platform composition and supervision methods of the safe operation of public transport vehicles in Zhenjiang3.This paper analyzes the characteristics of bus warnings under different weather,different road segments,different time periods and different driver characteristics based on Zhenjiang bus warning data.The K-means clustering algorithm is used to divide the drivers with different driving tendencies into three types.The early warning data is used to explore the influence mechanism of various factors on the early warning of bus,which lays a foundation for the establishment of the risk assessment model for the safe operation of public transport vehicles.4.Taking Zhenjiang bus operation data as the analysis sample,this paper extracts 7 important variables,including weather,time,speed,age,driving age,gender and education background.Using the principle of BP neural network,by adjusting the parameters,the data samples are trained continuously,and the driver fatigue risk prediction model and the vehicle abnormal state risk prediction model are established respectively,and realize the prediction of driver fatigue driving,vehicle abnormal state risk and the prevention and control of public transport vehicle operation risk in advance.5.The evaluation system of public transport vehicle operation safety based on AHP is established.Combined with the real-time early warning data of public transport vehicle,the quantitative evaluation of public transport vehicle operation risk is realized.The evaluation method can be used in the dynamic evaluation of public transport vehicle operation risk state.