Skip to content
Automated Human Mobility Mode Detection Based on GPS Tracking Data
C++ C
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Debug
Release
res
DialogPara.cpp
DialogPara.h
GPoint.cpp
GPoint.h
InputPara.cpp
InputPara.h
MainFrm.cpp
MainFrm.h
README.md
Segment.cpp
Segment.h
TravelMode_Classifier.aps
TravelMode_Classifier.cpp
TravelMode_Classifier.h
TravelMode_Classifier.rc
TravelMode_Classifier.vcxproj
TravelMode_Classifier.vcxproj.filters
TravelMode_Classifier.vcxproj.user
TravelMode_ClassifierDoc.cpp
TravelMode_ClassifierDoc.h
TravelMode_ClassifierView.cpp
TravelMode_ClassifierView.h
UserImages.bmp
resource.h
stdafx.cpp
stdafx.h
targetver.h

README.md

TravelMode Classifier

The mobility mode implied in volunteered raw GPS tracking data can provide us with valuable information to understand the user. In this paper, we propose an approach that can be used to detect mobility mode by mining/analyzing the geographic location, duration, speed as well as spatial context information in the data. Other ancillary GIS layers such as building footprints are also included to help classify the data. Our approach has been tested on three datasets. Five mobility modes: “transporting”, “parking”, “walking”, “roaming” and “indoor” are detected in our results. A quantitative comparison with the human interpretation results suggested an overall accuracy of 95%. The proposed approach is expected to facilitate the research of many geographic communities and development of location-based service.

You can’t perform that action at this time.