Identifying Transportation Modes from Raw GPS Data

• 作者: Min Fu Min Zhu Mingzhao Li Qihong Gan Qiuhui Zhu Zhenghao Zhou Zhibiao Huang
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: GPS TRANSPORTATION mode RANDOM FOREST CLASSIFIER
• 年，卷（期）: 2016,（1）
• 页码范围: 100-102
• 页数: 3页
• 分类号: C5

摘要

Raw Global Positioning System (GPS) data can provide rich context information for behaviour understanding and transport planning. However, they are not yet fully understood, and fine-grained identification of transportation mode is required. In this paper, we present a robust framework without geographic information, which can effectively and automatically identify transportation modes including car, bus, bike and walk. Firstly, a trajectory segmentation algorithm is designed to divide raw GPS trajectory into single mode segments. Secondly, several modern features are proposed which are more discriminating than traditional features. At last, an additional postprocessing procedure is adopted with considering the wholeness of trajectory. Based on Random Forest classifier, our framework can achieve a promising accuracy by distance of 82.85% for identifying transportation modes and especially 91.44% for car mode.