Research On Monitoring Abnormal Data Of Expressway Toll Station And Early Warning Of Fee Evasion Data

With the rapid development of China's economy,the pace of highway construction has also accelerated,and the provincial intranet and inter-provincial network toll collection of highway has gradually been widely implemented.However,how to prevent the high loss of tolls after networking charges,and carry out the inspection of Evasion under the new situation,has become a severe test for Expressway managers.Based on the toll station data of Inner Mongolia Expressway,after analyzing the overall traffic conditions of Inner Mongolia Expressway,this paper focuses on four kinds of abnormal behaviors,namely,inconsistent license plates,inconsistent types of vehicles,inconsistent types and unusual weight.Then select the index that can measure the abnormal behavior,and use PAM clustering algorithm to construct the monitoring model of the abnormal data of Inner Mongolia expressway toll station,so as to identify the escaping data.Secondly,this paper filters and constructs appropriate variables from two aspects of vehicle characteristics and driving characteristics,and constructs an early warning model of toll evasion data of Inner Mongolia Expressway Toll Station by using Logistic regression algorithm.It determines that the early warning probability is 0.469,that is,once the vehicle reaches or approaches the early warning probability,it should pay attention to the traffic cost,the charging weight,whether it is the vehicle and the type of passenger and cargo in Inner Mongolia.The IC card type and whether it is a free car are the six indicators,and the vehicle is checked in order to avoid the occurrence of cost evasion.In this paper,data mining and data analysis methods are used to analyze the abnormal behavior of expressway,mining the existing data of escaping fees,so as to provide an effective data basis for the management of escaping fees on expressway,improve the efficiency of the current toll inspection of expressway operation management units,and recover the economic losses under high escaping fees.