文章基本信息

标题：Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm
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作者：Fang Zong ; Yu Bai
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2015
卷号：6
期号：2
页码：212-227
DOI：10.3390/info6020212
语种：English
出版社：MDPI Publishing
摘要：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.
关键词：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