Correlation Analysis Of Driving Behavior Based On Correlation Analysis And Frequent Pattern Mining

Nowadays,the competition in the automotive industry is very fierce.How to improve the competitiveness of its products is a problem faced by all auto companies.With the development of the Internet of Vehicles technology,auto companies realize that it is also a good choice to improve the driver's driving experience from the software point of view.Therefore,many auto companies hope to provide users with a better driving experience by developing some intelligent reminder services.Therefore,this paper provides decision support for automotive companies to explore intelligent reminder services through the study of the relationship between people(health),cars,travel(traffic,date,time,destination,etc.).First,collect the driver's health data through the bracelet,including pressure,fatigue,blood pressure,etc.,collect GPS data through Android mobile APP,user-applied APP trip data,number of passengers,type of passengers,etc.,through OBD(vehicle Diagnostic system)collects the trip data of the vehicle,including ignition,flameout,operation,number of sudden accelerations,number of sudden decelerations,etc.;then,develops data quality assessment indicators to evaluate data quality,including data integrity,accuracy,and effectiveness.Sex,relevance,redundancy,etc.,and processing the abnormal sample data;then,propose the relevance analysis hypothesis,and use the spearman correlation coefficient in the correlation analysis method to analyze the correlation of the numerical type data,using correlation The chi-square test in the analysis method performs correlation analysis on the classification type data.Finally,the Apriori algorithm is used to mine the potential association relationship that is not proposed in the hypothesis.The analysis results show that people's physical condition,mood,date and other factors do have a certain correlation with driving behavior and travel destination.