Intelligent Analysis And Research Of Traffic And Transportation Comprehensive Administrative Law Enforcement Gases Based On Ariyun Big Data Platform

The comprehensive and accurate data of traffic and transportation comprehensive law enforcement cases is the basis of comprehensive administrative law enforcement work.In the traffic and transport administrative law enforcement industry,the establishment of traffic and transport administrative law enforcement model can be effective and in-depth analysis of the characteristics of administrative illegal cases.And for administrative law enforcement officials and policy makers to provide reference suggestions.Therefore,with the in-depth integration and development of big data technology in various fields,it is of practical significance to establish big data model of traffic and transportation comprehensive administrative law enforcement cases.Ariyun big data platform is widely used in many fields of industry,its machine learning PAI platform has advanced data analysis ability and machine learning ability,and it can be calculated by statistics.Learning a large number of historical data to generate empirical models,using empirical models to guide business.Ariyun Machine Learning platform is a machine learning platform based on Ariyun MaxCompute computing platform,which integrates data preprocessing,modeling and prediction.It is very suitable for building and analyzing the models used to predict the types of traffic and transportation comprehensive administrative law enforcement cases,so that artificial intelligence can serve the administrative management of government departments.The focus of this dissertation of academic degree is to collect the traffic and transportation comprehensive administrative law enforcement cases from 2012 to 2018 in Kaiping City,Guangdong Province,and predict the illegal types of traffic illegal cases in this area through several machine learning methods.In this paper,six characteristics of illegal case data,such as illegal time,illegal place and illegal subject,are extracted to predict the administrative classification of illegal cases,and a logical regression(logistic regression),naive Bayesian classification(Naive Bayesian Model),is established to predict the administrative classification of illegal cases.GBDT logical regression model,using obfuscation matrix and ROC The curve compares the model.It is found that the,GBDT LR model has agood experimental effect on predicting the types of traffic and transportation illegal cases,followed by a logical regression model and a naive Bayesian classification model.In the construction of GBDT LR classification model,the machine learning method can be used to mine new features,and the nonlinear features of GBDT can be effectively transformed into linear features by means of feature coding.The results show that the experimental model of,GBDT LR has a good classification effect on the illegal types of traffic and transportation administrative illegal cases,which is more effective than the simple statistical method.Big data predicted that mining technology can more accurately find out the internal relationship between input characteristics and output results,and can classify illegal cases according to different administrative categories,and then guide administrative acts to be more reasonable and efficient.It has the function of assisting law enforcement officers in handling cases.Also,the experimental model can be extended to Guangdong Province,the national transportation law enforcement system,through the national transportation illegal case big data,further optimize the effect of the model.