Next Article in Journal
Effect of the Long-Term Mean and the Temporal Stability of Water-Energy Dynamics on China’s Terrestrial Species Richness
Next Article in Special Issue
Reducing Building Conflicts in Map Generalization with an Improved PSO Algorithm
Previous Article in Journal
An Original Application of Image Recognition Based Location in Complex Indoor Environments
Previous Article in Special Issue
Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(2), 57; doi:10.3390/ijgi6020057

Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers

1,2
,
1
,
1,* and 1,2
1
Key Laboratory of Spatial Information Processing and Application System Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Science, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Academic Editors: Shih-Lung Shaw, Qingquan Li, Yang Yue and Wolfgang Kainz
Received: 3 February 2017 / Revised: 3 February 2017 / Accepted: 18 February 2017 / Published: 22 February 2017
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
View Full-Text   |   Download PDF [5911 KB, uploaded 22 February 2017]   |  

Abstract

Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS) information, which is not possible in many cases. In this paper, an approach based on ensemble learning is proposed to infer hybrid transportation modes using only Global Position System (GPS) data. First, in order to distinguish between different transportation modes, we used a statistical method to generate global features and extract several local features from sub-trajectories after trajectory segmentation, before these features were combined in the classification stage. Second, to obtain a better performance, we used tree-based ensemble models (Random Forest, Gradient Boosting Decision Tree, and XGBoost) instead of traditional methods (K-Nearest Neighbor, Decision Tree, and Support Vector Machines) to classify the different transportation modes. The experiment results on the later have shown the efficacy of our proposed approach. Among them, the XGBoost model produced the best performance with a classification accuracy of 90.77% obtained on the GEOLIFE dataset, and we used a tree-based ensemble method to ensure accurate feature selection to reduce the model complexity. View Full-Text
Keywords: trajectory data; GPS; ensemble model; XGBoost; feature importance trajectory data; GPS; ensemble model; XGBoost; feature importance
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Xiao, Z.; Wang, Y.; Fu, K.; Wu, F. Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers. ISPRS Int. J. Geo-Inf. 2017, 6, 57.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top