基于随机森林的高速公路变路径偷逃费行为识别研究
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1.中公华通(北京)科技发展有限公司;2.北京交通大学 交通运输学院;3.北京交通大学

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U495

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Identification of Fee Evasion Behavior in Expressway Changing Path Based on Random Forest
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TransChina(Beijing) Technology Co.,Ltd.

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    摘要:

    为提高高速公路变路径偷逃费行为识别效率,本文针对改变路径偷逃费行为进行研究,建立了一种基于随机森林的高速公路变路径偷逃费行为识别模型,能够有效识别该类偷逃费行为,协助高速公路相关管理部门追缴偷逃费用。首先,分析原始收费数据,筛选出与本研究相关的字段,经过运算得到12个模型可输入的初始特征;然后,通过计算各个特征的VIF和TOL值来剔除存在共线性的特征,并利用Boruta算法筛选高重要性特征(“行驶方向是否一致”、“入出站是否一致”、“通行时间”和“最小费额里程”);其次,使用SMOTETomek综合采样技术来平衡数据集;再其次,利用网格搜索法对随机森林进行超参数调优;最后,利用本文所建立模型进行训练和识别,并与基准模型的识别效果进行对比。结果表明,本文所建立模型能够更好地对高速公路变路径偷逃费行为进行识别,Macro-F1分数达到了0.966,优于XGBoost模型(0.9431)、DT模型(0.9563)和GBDT模型(0.9382),能够为运营管理部门稽查该类偷逃费车辆提供参考。

    Abstract:

    In order to improve the efficiency of identifying toll evasion behavior by changing paths on highways, this paper conducts research on toll evasion behavior by changing paths. A model for identifying toll evasion behavior by changing paths on highways based on random forests is established, which can effectively identify such behavior of toll evasion and assist relevant management departments of highways in recovering evaded fees. Firstly, the original toll data are analyzed to filter out the fields related to this study, and the 12 initial features that can be inputted into the model are obtained after arithmetic; Secondly, the features with covariance are eliminated by calculating the VIF and TOL values of each feature, and the Boruta algorithm is used to filter out the high-importance features ("whether the driving direction is consistent", "whether the entry and exit stations are consistent", "travel time", and "minimum fare mileage"); Thirdly, the data set is balanced using the SMOTETomek integrated sampling technique; Then, the grid search method is used to tune the hyperparameters of the random forest; Finally, the model built in this paper is utilized for training and recognition, and the recognition effect is compared with that of the benchmark model. The results show that the model developed in this paper can better recognize the toll evasion behavior by changing paths on highways, and the Macro-F1 score reaches 0.966, which is better than the XGBoost model (0.9431), the DT model (0.9563), and the GBDT model (0.9382), and it can provide reference for operation management departments to inspect such toll evasion vehicles.

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邹杰,曹宏禄,李平安,等. 基于随机森林的高速公路变路径偷逃费行为识别研究[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-10-21
  • 最后修改日期:2024-05-23
  • 录用日期:2024-05-29
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