Title: Transportation modes behaviour analysis based on raw GPS dataset

Authors: Qiuhui Zhu; Min Zhu; Mingzhao Li; Min Fu; Zhibiao Huang; Qihong Gan; Zhenghao Zhou

Addresses: College of Computer Science, Sichuan University, Chengdu, China ' College of Computer Science, Sichuan University, Chengdu, China ' School of Science, GPO Box 2476 Melbourne VIC 3001, Australia ' College of Computer Science, Sichuan University, Chengdu, China ' Chengdu Institute of Computer Application, Chinese Academy of Sciences, No. 11, 4th Section, South Renmin Road, Wuhou District, Chengdu, China ' Modern Education Technology Center, Sichuan University, Chengdu, China ' High School No. 7, No. 1 Linyin Middle Street, Wuhou District, Chengdu, China

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.

Keywords: global positioning system; GPS; transition point; transportation mode; random forest classifier.

DOI: 10.1504/IJES.2018.090569

International Journal of Embedded Systems, 2018 Vol.10 No.2, pp.126 - 136

Received: 15 Jun 2016
Accepted: 30 Aug 2016

Published online: 22 Mar 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article