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应用科学学报 ›› 2024, Vol. 42 ›› Issue (5): 857-870.doi: 10.3969/j.issn.0255-8297.2024.05.012

• 计算机科学与应用 • 上一篇    

基于RFE-OPTUNA-XGBoost模型的高速公路逃费模式识别

马飞虎1,2, 雷皓安1, 孙翠羽1,2, 罗佳洁1   

  1. 1. 华东交通大学 交通运输工程学院, 江西 南昌 330013;
    2. 江西省智能交通基础设施工程研究中心, 江西 南昌 330013
  • 收稿日期:2023-04-14 发布日期:2024-09-29
  • 通信作者: 马飞虎，副教授，研究方向为交通地理信息、智能交通、3S技术集成与应用。E-mail:mfh@ecjtu.edu.cn E-mail:mfh@ecjtu.edu.cn
  • 基金资助:
    国家重点研发计划项目（No.021YFE0105600）；国家自然科学基金面上项目（No.51978263）；江西省自然科学基金重点项目（No.20192ACBL20008）资助

Highway Toll Evasion Patterns Identification Based on RFE-OPTUNA-XGBoost Model

MA Feihu1,2, LEI Haoan1, SUN Cuiyu1,2, LUO Jiajie1   

  1. 1. School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China;
    2. Jiangxi Intelligent Transportation Infrastructure Engineering Research Center, Nanchang 330013, Jiangxi, China
  • Received:2023-04-14 Published:2024-09-29

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摘要： 受经济利益驱动，中国高速公路逃费行为频繁发生。为此，该文选取了2020年某研究区域的脱敏通行数据，通过数据挖掘分析逃费车辆的行为特征，提出一种基于递归特征消除算法和OPTUNA优化框架的极限梯度提升树（recursive feature elimination-OPTINA-extreme gradient boosting,RFE-OPTUNA-XGBoost）的逃费模式识别模型，该识别模型准确率达到了0.945，各逃费方式的平均接受者操作特性曲线下面积值（area under curve,AUC）分别为：大车小标0.997、U/J型0.980、假绿通0.969、冲岗0.924。结果证明，基于RFE-OPTUNA-XGBoost的模型对于逃费模式识别的准确程度及各逃费模式的AUC值都更高。综上所述，提出的基于RFE-OPTUNA-XGBoost的高速公路逃费车辆逃费的识别模型能精准识别逃费模式。在实际应用中，对于高速公路管理部门展开稽查工作具有重大现实意义。

关键词: 高速公路, 逃费模式识别, 数据挖掘, 机器学习

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

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