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
[1] Cheung S Y, Varaiya P. Traffic surveillance by wireless sensor networks: final report[R]. California: University of California, Berkeley,2007.
[2] Kafai M, Bhanu B. Dynamic bayesian networks for vehicle classifcation in video[J]. IEEE Transactions on Industrial Informatics,2012,20(1):100-109.
[3] Rajesh P, Geetha M K, Ramu R. Traffic density estimation, vehicle classification and stopped vehicle detection for traffic surveillance system using predefined traffic videos[J]. Computer Science and Engineering,2013,56:13671-13676.
[4] Guo B F, Nixon M S, Damarla T R. Acoustic information fusion for ground vehicle classifcation[C]∥The 11th International Conference on Information Fusion,Cologne: Institute of Electrical and Electronics Engineers Inc,2008:1-7.

[5] Paulraj M P, Adoma A H, Sundararaj S, et al. A. Moving vehicle recognition and classification based on time domain approach[J]. Procedia Engineering,2013,53:405-410.
[6] 陈强,李江,王双维,等.基于AR模型的车型自动分类技术[J].吉林大学学报:工学版,2007,37(2):325-328.
Chen Qiang, Li Jiang, Wang Shuang-wei, et al. Automatic vehicle classification by radiated noise of vehicles based on AR model[J]. Journal of Jilin University (Engineering and Technology Edition),2007,37(2):325-328.
[7] Jeng S T, Chu L, Hernandez S V. A wavelet-KNN based vehicle classification approach using inductive loop signatures[C]∥Transportation Research Board 92nd Annual Meeting,Washington: Transportation Research Board,2013.
[8] Coifman B, Kimb S. Speed estimation and length based vehicle classication from freeway single-loop detectors[J]. Transportation Research Part C,2009,17(4):349-364.
[9] Kaewkamnerd S, Pongthornseri R, Chinrungrueng J, et al. Automatic vehicle classification using wireless magnetic sensor[C]∥IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications,Rende: Institute of Electrical and Electronics Engineers Inc,2009:420-424.
[10] 杨兆升,杨志宏,赵丹华.高速公路收费系统的车型自动分类[J].吉林大学学报:工学版,2002,32(3): 6-9.
Yang Zhao-sheng, Yang Zhi-hong, Zhao Dan-hua. Research on vehicle auto classification of freeway toll collection system[J]. Journal of Jilin University (Engineering and Technology Edition),2002,32(3):6-9.
[11] Li Hai-jian, Dong Hong-hui, Jia Li-min, et al. Some practical vehicle speed estimation methods by a single traffic magnetic sensor[C]∥The 14th International IEEE Conference on Intelligent Transportation Systems,Washington: Institute of Electrical and Electronics Engineers Inc,2011:1566-1573.
[12] Wu Xin-dong, Kumar V. CART: Classication and Regression Trees[M]. London:Chapman and Hall/CRC,2009:179-207.
[13] Quinlan J R. Induction of decision trees[J]. Machine Learning,1986,1(1):81-106.
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