基于K最近邻的高速公路偷逃费事件识别

Recognition of Highway Toll Evasion Events Based on K-nearest Neighbor

为解决现有高速公路收费稽查的低效率和高成本等问题,提出一种基于K最近邻算法(k-nearest neighbor,KNN)的偷逃费事件识别模型。首先分析高速公路大车小标逃费行为特性,设计并建立识别与校核算法,从原始收费数据中提取出逃费样本。其次,对原始逃费数据进行预处理后,采用主成分分析法进行特征降维。最后针对逃费数据集不同类别样本数分布的极端不平衡特点,使用过采样方法中的改进的合成少数类过采样算法(borderline2 synthetic minority over-sampling technique,BorderlineSMOTE2)对数据做平衡处理,再通过KNN算法建立逃费行为分类识别模型。最终验证结果表明,所建立的逃费行为识别模型识别精确率为0.75,召回率为0.84,f 1系数为0.79,表明该算法针对逃费行为样本分类识别精度较高,模型性能较好。基于KNN的高速公路车辆偷逃费事件识别模型针对逃费数据的高维不平衡特点建立了相应的处理规则与算法,提高了识别精度,识别结果可助力高速公路收费稽核有效筛查逃费行为,降低通行费损失成本。

In order to address the problems of low efficiency and high cost in the existing highway toll inspection,a K-nearest neighbor(KNN)-based recognition model of toll evasion events was proposed.Firstly,the characteristics of toll evasion behavior of large vehicles but small signs on highways were analyzed,the recognition and verification algorithms were designed and established to extract toll evasion samples from the original toll data.Secondly,after preprocessing the original evasion data,principal component analysis was used for feature dimensionality reduction.Finally,in view of the extreme imbalance of sample size distribution in different categories of the evasion dataset,the borderline2 synthetic minority over-sampling technique algorithm(BorderlineSMOTE2)in the oversampling method was used to balance the data,and the KNN algorithm was used to establish a classification and recognition model for evasion behavior.The final verification results show that the recognition accuracy rate of the established toll evasion behavior recognition model is 0.75,the recall rate is 0.84,and the f 1-score is 0.79,indicating that the model has a higher classification and recognition accuracy for the toll evasion behavior samples and better model performance.The recognition model for highway vehicle evasion incidents based on KNN has established corresponding processing rules and algorithms to address the high-dimensional imbalance characteristics of evasion data,improving recognition accuracy.The recognition results can assist highway toll inspection in effectively screening evasion behavior and reducing the cost of toll loss.