Volume XXXVIII-4/W25
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-4/W25, 87-93, 2011
https://doi.org/10.5194/isprsarchives-XXXVIII-4-W25-87-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-4/W25, 87-93, 2011
https://doi.org/10.5194/isprsarchives-XXXVIII-4-W25-87-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 Aug 2012

30 Aug 2012

MULTI-STAGE APPROACH TO TRAVEL-MODE SEGMENTATION AND CLASSIFICATION OF GPS TRACES

L. Zhang, S. Dalyot, D. Eggert, and M. Sester L. Zhang et al.
  • Institut für Kartographie und Geoinformatik (IKG), Leibniz Universität Hannover, Appelstraße 9a, 30167 Hannover, Germany

Keywords: Acquisition, Data mining, Pattern, Recognition, Classification, GPS/INS, Segmentation, Mapping

Abstract. 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.