Transportation modes behaviour analysis based on raw GPS dataset. (2018)

• Record Type: Journal Article
• Authors: Zhu, Qiuhui; Zhu, Min; Li, Mingzhao; Fu, Min; Huang, Zhibiao; Gan, Qihong; Zhou, Zhenghao
• Is Part Of: International journal of embedded systems. Volume 10:Number 2(2018)
• Page Start: 126
• Page End: 136
• Publication Date: 2018
• Subjects: global positioning system -- GPS -- transition point -- transportation mode -- random forest classifier; Embedded computer systems -- Periodicals; 004.16
• Languages: English
• ISSNs: 1741-1068

Abstract

Significant information exists in the global positioning system (GPS) data for understanding behaviours and transport planning. However, fine-grained identification of transportation modes is still required. In this paper, we present a robust framework to identify different means of transportation modes from raw GPS dataset. We make the following contributions: 1) we design an effective trajectory segmentation algorithm to divide raw GPS trajectory into single mode segments based on logical assumptions; 2) we propose several modern features, which are more discriminating than traditional features; 3) we adopt an additional segments expansion procedure by considering the wholeness of trajectory. Experiments prove that our framework achieves a promising accuracy for identifying transportation modes.