Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 87-93 |
| Seitenumfang | 7 |
| Fachzeitschrift | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Jahrgang | 38 |
| Ausgabenummer | 4W25 |
| Publikationsstatus | Veröffentlicht - 30 Aug. 2011 |
| Veranstaltung | ISPRS Guilin 2011 Workshop on Geospatial Data Infrastructure: From Data Acquisition and Updating to Smarter Services - Guilin, China Dauer: 20 Okt. 2011 → 21 Okt. 2011 |
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
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in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Jahrgang 38, Nr. 4W25, 30.08.2011, S. 87-93.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Multi-stage approach to travel-mode segmentation and classification of GPS traces
AU - Zhang, Lijuan
AU - Dalyot, Sagi
AU - Eggert, Daniel
AU - Sester, Monika
PY - 2011/8/30
Y1 - 2011/8/30
N2 - 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.
AB - 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.
KW - Acquisition
KW - Classification
KW - Data mining
KW - GPS/INS
KW - Mapping
KW - Pattern
KW - Recognition
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=84887317274&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:84887317274
VL - 38
SP - 87
EP - 93
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - 4W25
T2 - ISPRS Guilin 2011 Workshop on Geospatial Data Infrastructure: From Data Acquisition and Updating to Smarter Services
Y2 - 20 October 2011 through 21 October 2011
ER -