The Research Of Early Warning Analysis On Transport Vehicles Based On Data Mining

With the rapid development of the economy and the rapid expansion of the city,the flow of traffic sustained increase.Traffic problems caused by a large amount of traffic data have become a problem that needs to be solved urgently.In the area of transportation,the data generated by road transport companies account for a large part of it,and it is also the most easily part to manage.The management of vehicles for road transport enterprises plays a crucial role in improving the state of traffic safety in China.Data mining technology is one of the important aspects of database knowledge discovery.The purpose is to mine potential high-value data from massive and complex data.The significance is that the knowledge generated by mining can be used for decision support,information management,scientific research,and so on.The research of data mining technology in the direction of traffic safety is very valuable.How to make use of the massive and complex traffic data and discover its hidden meaning have always been the focus of traffic data research.This thesis studies the vehicle alarm data of mass vehicles in transport enterprises in Yunnan Provice based on the data mining technology.Analyze and process data based on Python.Analyze vehicle alarm data of mass transportation companies in terms of type,time,area,and mine meaningful information.Use LSTM time series model to predict vehicle alarm conditions,analysis the specific situation of each car deeply to put forward targeted warning information.Combined with Web GIS visualization technology,Baidu map API to visualize the results of data mining.This thesis analyzes the data mining results of transportation vehicles in depth and proposes corresponding decision support.Design a visualization platform to display the overall analysis results.The purpose is to find out the direction of key management,and it has application significance to ease the traffic safety situation in Yunnan Province.