Multi-stage approach to travel-mode segmentation and classification of gps traces

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Zhang, Lijuan; Dalyot, Sagi; Eggert, Daniel; Sester, Monika: Multi-stage approach to travel-mode segmentation and classification of gps traces. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: [Geospatial Data Infrastructure: From Data Acquisition And Updating To Smarter Services] 38-4 (2011), Nr. W25, S. 87-93. DOI: https://doi.org/10.5194/isprsarchives-XXXVIII-4-W25-87-2011

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/1143

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Sum total of downloads: 572




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This paper presents a multi-stage approach toward the robust classification of travel-modes from GPS traces. Due to the fact that GPS traces are often composed of more than one travel-mode, they are segmented to find sub-traces characterized as an individual travel-mode. This is conducted by finding individual movement segments by identifying stops. In the first stage of classification three main travel-mode classes are identified: pedestrian, bicycle, and motorized vehicles; this is achieved based on the identified segments using speed, acceleration and heading related parameters. Then, segments are linked up to form sub-traces of individual travel-mode. After the first stage is achieved, a breakdown classification of the motorized vehicles class is implemented based on sub-traces of individual travel-mode of cars, buses, trams and trains using Support Vector Machines (SVMs) method. This paper presents a qualitative classification of travel-modes, thus introducing new robust and precise capabilities for the problem at hand.
License of this version: CC BY 3.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2011
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 149 26.05%
2 image of flag of United States United States 70 12.24%
3 image of flag of China China 35 6.12%
4 image of flag of United Kingdom United Kingdom 27 4.72%
5 image of flag of France France 23 4.02%
6 image of flag of Israel Israel 20 3.50%
7 image of flag of Austria Austria 18 3.15%
8 image of flag of Netherlands Netherlands 17 2.97%
9 image of flag of Spain Spain 17 2.97%
10 image of flag of Canada Canada 17 2.97%
    other countries 179 31.29%

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