Improved waveform‐feature‐based vehicle classification using a single‐point magnetic sensor. Issue 5 (18th December 2014)

• Record Type: Journal Article
• Authors: He, Yao; Du, Yuchuan; Sun, Lijun; Wang, Yibing
• Is Part Of: Journal of advanced transportation. Volume 49:Issue 5(2015)
• Page Start: 663
• Page End: 682
• Publication Date: 2014-12-18
• Subjects: vehicle classification -- magnetic sensor -- clustering support vector machine -- particle swarm optimization; Transportation -- Periodicals; 388.05
• DOI: 10.1002/atr.1299
• Languages: English
• ISSNs: 0197-6729

Abstract

Summary: Vehicle classification systems have important roles in applications related to real‐time traffic management. They also provide essential data and necessary information for traffic planning, pavement design, and maintenance. Among various classification techniques, the length‐based classification technique is widely used at present. However, the undesirable speed estimates provided by conventional data aggregation make it impossible to collect reliable length data from a single‐point sensor during real‐time operations. In this paper, an innovative approach of vehicle classification will be proposed, which achieved very satisfactory results on a single‐point sensor. This method has two essential parts. The first concerns with the procedure of smart feature extraction and selection according to the proposed filter–filter–wrapper model. The model of filter–filter–wrapper is adopted to make an evaluation on the extracted feature subsets. Meanwhile, the model will determine a nonredundant feature subset, which can make a complete reflection on the differences of various types of vehicles. In the second part, an algorithm for vehicle classification according to the theoretical basis of clustering support vector machines (C‐SVMs) was established with the selected optimal feature subset. The paper also uses particle swarm optimization (PSO), with the purpose of searching for an optimal kernel parameter and the slack penalty parameter in C‐SVMs. A total of 460 samples were tested through cross validation, and the result turned out that the classification accuracy was over 99%. In summary, the test results demonstrated that our vehicle classification method could enhance the efficiency of machine‐learning‐based data mining and the accuracy of vehicle classification. Copyright © 2014 John Wiley & Sons, Ltd.