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Title: Trip Purpose Identification from GPS Tracks
Accession Number: 01516765
Record Type: Component
Record URL: Availability: Transportation Research Board Business Office 500 Fifth Street, NW Find a library where document is available Abstract: Travel surveys are increasingly taking advantage of GPS data, which offer precise route and time observations and a potentially reduced response burden. In these surveys, travel diaries are usually constructed automatically where research on the employed procedures has been focused on mode identification. The goal of the research reported here was to improve trip purpose identification. The analysis used random forests, a machine-learning approach that had been successfully applied to mode identification. The analysis was based on GPS tracks and accelerometer data collected by 156 participants who took part in a 1-week travel survey in Switzerland that was completed in 2012. The results show that random forests provide robust trip purpose classification. For ensemble runs, the share of correct predictions was between 80% and 85%. Different setups of the classifier were possible and sometimes required by the application context. The training set and its input variables (feature set) of the classifier were defined in various ways. Four relevant setups were tested for this study.
Monograph Title: Monograph Accession #: 01530618
Report/Paper Numbers: 14-2657
Language: English
Authors: Montini, LaraRieser-Schüssler, NadineHorni, AndreasAxhausen, Kay WPagination: pp 16–23
Publication Date: 2014
ISBN: 9780309294980
Media Type: Print
Features: Figures
(5)
; References
(30)
; Tables
(2)
TRT Terms: Uncontrolled Terms: Geographic Terms: Subject Areas: Data and Information Technology; Highways; Passenger Transportation; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning
Files: TRIS, TRB, ATRI
Created Date: Jan 27 2014 2:55PM
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