计算机与现代化

• 人工智能 • 上一篇    下一篇

基于SVM的新能源公交车运营里程核查方法

  

  1. (长安大学经济与管理学院,陕西西安710061)
  • 收稿日期:2019-12-08 出版日期:2020-05-20 发布日期:2020-05-21
  • 作者简介:张文华(1994-),女,甘肃天水人,硕士研究生,研究方向:交通运输规划与管理,E-mail: 2994833086@qq.com; 张志俊(1970-),男,陕西咸阳人,教授,博士,研究方向:统计,E-mail: zzj009@126.com。

SVM-based Verification Method for New  Energy Bus Operation Mileage

  1. (School of Economics and Management, Chang’an University, Xi’an 710061, China)
  • Received:2019-12-08 Online:2020-05-20 Published:2020-05-21

摘要: 新能源公交车运营里程核查是新能源公交车补贴申报工作的重要环节,核查申报数据存在工作量大,效率低等问题。本文对申报数据进行描述性分析和单因素方差分析,得出车长与车辆年运营里程有关系。基于此,利用线性SVM算法对运营里程申报数据进行有监督分类,采用线性核函数,通过训练SVM,选择合适惩罚参数C,构建最优SVM,从而识别出可疑的值。结果表明,线性SVM算法能够更有效地检测出年运营里程可疑的车辆,为新能源公交车运营里程核查工作提供参考依据。

关键词: 新能源公交车运营里程核查, 异常点检测, 支持向量机, 线性核函数

Abstract: The verification of new energy bus operation mileage is an important part of the new energy bus subsidy declaration work. The verification of declaration data has problems, such as large workload and low efficiency. In this paper, descriptive analysis and one-way ANOVA of the declared data show that the length of the vehicle is related to the annual operating mileage of the vehicle. Based on this, the linear SVM algorithm is used to supervise the operational mileage declaration data. By using the linear kernel function, training the SVM and selecting the appropriate penalty parameter C, the optimal SVM is constructed to identify suspicious values. Experimental results show that the linear SVM algorithm can detect vehicles with suspicious annual operating mileage more effectively, which can provide reference for the verification of new energy bus operation mileage.

Key words: new energy bus operation mileage verification, abnormal point detection, SVM, linear kernel function

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