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Information 2015, 6(2), 212-227; doi:10.3390/info6020212

Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

College of Transportation, Jilin University, Changchun 130022, China
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Author to whom correspondence should be addressed.
Academic Editors: Baozhen Yao and Yudong Zhang
Received: 15 April 2015 / Revised: 21 May 2015 / Accepted: 27 May 2015 / Published: 4 June 2015
(This article belongs to the Special Issue Swarm Information Acquisition and Swarm Intelligence in Engineering)
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Abstract

Travel mode identification is one of the essential steps in travel information detection with Global Positioning System (GPS) survey data. This paper presents a Support Vector Classification (SVC) model for travel mode identification with GPS data. Genetic algorithm (GA) is employed for optimizing the parameters in the model. The travel modes of walking, bicycle, subway, bus, and car are recognized in this model. The results indicate that the developed model shows a high level of accuracy for mode identification. The estimation results also present GA’s contribution to the optimization of the model. The findings can be used to identify travel mode based on GPS survey data, which will significantly enhance the efficiency and accuracy of travel survey and data processing. By providing crucial trip information, the results also contribute to the modeling and analyzing of travel behavior and are readily applicable to a wide range of transportation practices. View Full-Text
Keywords: Global Positioning System (GPS); travel survey; travel mode; Support Vector Classification; genetic algorithm Global Positioning System (GPS); travel survey; travel mode; Support Vector Classification; genetic algorithm
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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).

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Zong, F.; Bai, Y.; Wang, X.; Yuan, Y.; He, Y. Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm. Information 2015, 6, 212-227.

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