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

• Orignal Article • Previous Articles     Next Articles

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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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

CLC Number: 

  • U491
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