Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and Python
The increasing popularity of global positioning systems (GPSs) has prompted transportation researchers to develop methods that can automatically extract and classify episodes from GPS data. This paper presents a transferable and efficient method of extracting and classifying activity episodes from GPS data, without additional information. The proposed method, developed using Python®, introduces the use of the multinomial logit (MNL) model in classifying extracted episodes into different types: stop, car, walk, bus, and other (travel) episodes. The proposed method is demonstrated using a GPS dataset from the Space-Time Activity Research project in Halifax, Canada. The GPS data consisted of 5127 person-days (about 47 million points). With input requirements directly derived from GPS data and the efficiency provided by the MNL model, the proposed method looks promising as a transferable and efficient method of extracting activity and travel episodes from GPS data.
- Record URL:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/1767712
- Abstract reprinted with permission of Taylor & Francis.
- Dalumpines, Ron
- Scott, Darren M
- Publication Date: 2017-7
- Media Type: Web
- Pagination: pp 523-539
- TRT Terms: Automobile travel; Bus transportation; Classification; Data collection; Global Positioning System; Mode choice; Walking
- Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Public Transportation;
- Accession Number: 01638818
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 26 2017 9:31AM