Abstract: Driven by economic benefits, highway toll evasion behavior occurs frequently in China. This study utilizes anonymized toll data from a specific region in 2020 to address this issue. Through data mining to analyze the behavior characteristics of the evading vehicles, we propose a toll evasion pattern recognition model based on RFE-OPTUNA-XGBoost. The accuracy of this recognition model reaches 0.945, with average AUC values for different evasion methods as follows: large vehicle misclassification at 0.997, U/J-turn evasion at 0.980, fake green pass at 0.969, and gate crashing at 0.924. The results demonstrate that the RFE-OPTUNA-XGBoost model achieves higher accuracy in toll evasion pattern recognition and higher AUC values for each evasion method. In summary, the proposed model can accurately identify toll evasion patterns, offering significant practical value for highway management departments in conducting inspections and preventing toll evasion.

Key words: highway, toll evasion patterns identification, data mining, machine learning