吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (1): 97-103.doi: 10.13229/j.cnki.jdxbgxb201501015

• 论文 • 上一篇    下一篇

面向城市道路的地磁传感器单节点车型分类

李海舰1，董宏辉1，史元超2，贾利民1，郭卫锋3   

  1. 1.北京交通大学 轨道交通控制与安全国家重点实验室， 北京 100044；
    2.中铁国际多式联运有限公司， 北京 100161；
    3.西昌卫星发射中心， 四川 西昌 571339
  • 收稿日期:2013-05-27 出版日期:2015-02-01 发布日期:2015-02-01
  • 通讯作者: 贾利民(1963),男,教授,博士生导师.研究方向:智能运输系统.E-mail:jialm@vip.sina.com
  • 作者简介:李海舰(1986)，男，博士研究生.研究方向:智能交通系统，交通传感器网络.E-mail:lihaijian0506@163.com
  • 基金资助:
    “863”国家高技术研究发展计划项目(2012AA112401,2011AA110505)；国家自然科学基金项目(61104164).

Vehicle classification with a single magnetic sensor for urban road

LI Hai-jian1,DONG Hong-hui1,SHI Yuan-chao2,JIA Li-min1,GUO Wei-feng3   

  1. 1.State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;
    2. China Railway International Multimodal Transport Co.,Ltd, Beijing 100161, China;
    3.Xichang Satellite Launch Center, Xichang 571339, China
  • Received:2013-05-27 Online:2015-02-01 Published:2015-02-01

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摘要： 提出了一种利用多功能地磁传感器采集道路环境磁场数据，并基于决策树模型实现车型的在线分类方法。文中提取8种与车速无关的车辆波形时域特征作为模型输入，基于最优最小划分样本数的CART算法对决策树模型进行训练。对训练得到的决策树，基于最小误差剪枝原则进行剪枝，得到具有更高样本鲁棒性的最佳剪枝树。通过在北京市某道路上布设地磁传感器获取了两种车型数据，正、反向测试的平均准确率分别为88.9%和94.4%。与现有多个分类方法进行了对比实验，结果表明:本文方法能够进行在线车型分类，并在分类准确率、样本鲁棒性和算法执行时间等方面更具优势，能够应用于实际城市道路现场进行车型分类。

关键词: 道路工程, 车型分类, 地磁传感器, CART算法, 决策树

Abstract: An online vehicle classification method based on decision tree model is proposed, in which multi-functional magnetic sensors are used to collect field magnetic data. First, eight speed-independent time-domain waveform features are extracted as the inputs of the decision tree model. Then the decision tree model is trained based on the Classification and Regression Tree (CART) algorithm with the Minimum Number of Slit-samples (MNS). Finally the trained decision tree model is pruned with a Minimum Error Pruning (MEP) role to obtain an optimal pruning tree, which is more robust to new samples. For the field samples collected from a road in Beijing with two types of vehicles, the average classification accuracies of the forward and reverse tests are 88.9% and 94.4% respectively. The proposed classification method is compared with existing methods. The results show that the proposed method enables online vehicle type classification with the advantages of high classification accuracy, sample robustness and less algorithm execution time.

Key words: road engineering, vehicle classification, magnetic sensor, CART algorithm, decision tree

中图分类号: 

  • U491
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