Journal
TRANSPORTATION RESEARCH RECORD
Volume -, Issue 2405, Pages 16-23Publisher
SAGE PUBLICATIONS INC
DOI: 10.3141/2405-03
Keywords
-
Funding
- European Union as part of the project PEACOX (Usable Persuasive Trip Advisor for Reducing CO2 Consumption), within the Seventh Framework Programme
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available