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Open AccessArticle

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

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

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ISPRS Int. J. Geo-Inf., EISSN 2220-9964, Published by MDPI AG
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