Vehicle classification with single multi-functional magnetic sensor and optimal MNS-based CART |
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Affiliation: | 1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;2. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;1. Universidade Federal do Rio de Janeiro - PEE-COPPE/Del-Poli/GTA, Brazil;2. Universidade Federal Fluminense - PPGEET/MídiaCom, Brazil;1. Institute of Informatics and Telematics (IIT), Italian National Research Council (CNR), Via G. Moruzzi 1, Pisa, Italy;2. Converging Networks Laboratory, VTT Technical Research Centre of Finland, Kaitoväylä 1, 90590 Oulu, Finland;1. Computer Science and System Engineering Department (DIIS), Campus of Teruel, University of Zaragoza, Ciudad Escolar s/n, 44003 Teruel, Spain;2. Computer Engineering Department (DISCA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain |
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Abstract: | To take advantages of magnetic sensor technology in terms of cost, size, weight, power consumption and wireless communication, a wireless multi-functional magnetic sensor was designed and developed. Then, a novel method with single multi-functional magnetic sensor and optimal Minimum Number of Split-sample (MNS)-based Classification and Regression Tree (CART) algorithm was proposed in this paper to classify on-road vehicles. The sensor was deployed on the road to acquire real-time vehicle waveform data. The decision tree model based on CART algorithm was used to execute on-line vehicle classification in the sensor node. Eight speed-independent time-domain waveform features were extracted as the model inputs. This paper trained the decision tree model by using vehicle samples derived from the multi-functional magnetic sensor and pruned the optimal decision tree with a Minimum Error Pruning (MEP) rule to obtain an optimal pruning tree which is more robust to new samples. Some experiments were implemented by different sample sets and classification methods. The results showed that the proposed method achieved on-line vehicle classification in the sensor node. For the field sample sets with two vehicle classes, the average accuracy rates of test samples were 88.9% and 94.4% in the original samples and swapping samples respectively. Besides higher accuracy, the method also has a better sample robustness, which is easy to classify new samples. The comparison results of current methods also showed that the proposed method has some advantages in aspects of accuracy rate, sample robustness and execution time. |
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Keywords: | Vehicle classification Multi-functional magnetic sensor CART algorithm Decision tree |
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