Research On Recognition Algorithm Of Toll Abnormal Vehicle Based On Big Data

A large amount of driving data is stored in the expressway toll collection system.How to get useful value information from massive data to help identify abnormal toll vehicles and reduce the loss of expressway operation is the focus of current research.The development of big data technology makes the traffic management increasingly convenient.By using data mining technology to analyze vehicle driving characteristics and extract important behavior attributes,abnormal vehicle recognition model can be stablished.It can significantly improve the efficiency of expressway operation and management and accurately inspect abnormal vehicles.In order to make the best use of the big data of expressway,this paper firstly conducts a systematic study on the expressway toll collection system to grasp the principle of data storage from the structure and function of the system.Then,analyzing the characteristics and conventional data model of online charging data effectively.At the same time,the phenomenon of evasion in abnormal vehicles is classified to help the recognition and prediction of evasion vehicles.The min-max normalization method is used to normalize the data and virtualize the type variables.And the SMOTE oversampling algorithm is used to balance the data set to prevent overfitting caused by data imbalance.After analyzing and processing the data,this paper establishes a prediction model of toll evasion behavior by combining random forest and logistic regression.Then using neural networks to accurately identify the type of fee evasion.And the prediction effect of the model is evaluated by confusion matrix and ROC curve.Finally,A high precision logistic regression model is established with model characteristic variables which selected by random forest algorithm variables.The experimental results show that the predicted recall rate is 94.71% and AUC value is96%.It is proved that the probability prediction model of the vehicle with evasion is of high stability and reliability.The results show that the effect of neural network on the identification of toll-escape vehicles is significant.The model recognition accuracy rate is 95%,and the recall rate of the model in each type of toll-economy recognition reaches more than 90%.With the expressway network toll data,the prediction model of vehicle with evasion is established through the combination of random forest algorithm,logistic regression algorithm and neural network model by adopting big data processing technology,which has a high application value for the recognition of the types of vehicles to evade the toll.