Prediction of toll evasion behaviors based on electronic toll collection data

In order to improve the efficiency of the audit of toll evasion, a toll evasion prediction model is established based on historical ETC(Electronic Toll Collection)portal flow transaction data. Now it has become the most perfect Internet of Things system for expressways, which can be used to strengthen the management of traffic toll supervision. First, the complete trajectory data set of vehicle travel was constructed based on the ETC transaction data, the trajectory data set of vehicle travel were divided into multiple sections for analysis. Second, density clustering was used to partition toll evasion data set. Finally, a random forest algorithm was used to construct a prediction model of toll evasion behaviors. The correct prediction ratio of the model for toll evasion behaviors was 91.3%.To avoid training over-fitting, ETC plate recognition data is used for proving and accurate discovery of toll evasion behaviors validates the feasibility of the method.