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Identifying Multiclass Vehicles Using Global Positioning System Data
| 题名： | Identifying Multiclass Vehicles Using Global Positioning System Data |
| 正文语种： | 英文 |
| 作者： | Zhanbo Sun; Xuegang (Jeff) Ban |
| 作者单位： | School of Transportation and Logistics, Southwest Jiaotong University;Department of Civil and Environmental Engineering, University of Washington |
| 关键词： | Multiclass Vehicle Classification;Global Positioning Systems (GPS); Support Vector Machine (SVM);Acceleration and Deceleration |
| 摘要： | It has been previously evidenced that Global Positioning System (GPS) data can be used to distinguish passenger cars from delivery trucks. In this paper, a machine learning approach is proposed to use GPS data to identify multiclass vehicles, including passenger cars, single unit trucks, and multi-trailer trucks. The method is acceleration and deceleration-based since it considers the variations of acceleration and deceleration as the most effective features to classify vehicles. The overall classification result for the three vehicle classes is about 75%. The major challenge is to distinguish single unit trucks from multitrailer trucks due to their somewhat similar mobility patterns. The paper also explores the impacts of GPS sampling frequency on vehicle classification. It is found that the proposed multiclass vehicle classification can be reasonably conducted if the data are collected frequently enough (i.e., every five seconds or more frequently) to capture the major acceleration and deceleration processes. The proposed method can be considered as a low-cost and non-intrusive approach to collect vehicle class information and to potentiallysupplement the existing classification schemes in urban areas. |
| 出版年： | 2018 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | 22 |
| 期： | 1 |
| 页码： | 1-9 |
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