A study of tour-based mode choice based on a Support Vector Machine classifier

Miriam Pirra and Marco Diana

Transportation Planning and Technology, 2019, vol. 42, issue 1, 23-36

Abstract: A new approach in recognizing travel mode choice patterns is proposed, based on the Support Vector Machine classification technique. The tour-based travel demand dataset that is analysed is for New York State, derived from the 2009 U.S. National Household Travel Survey. The main features characterizing each tour are the means used, travel-related variables and socioeconomic aspects. Results obtained demonstrate the ability to predict to some extent, in real settings where car use dominates, which tours are likely to be made by public transport or non-motorized means. Moreover, the flexibility of the technique allows assessing the predictive power of each feature according to the combination of travel means used in different tours. Potential applications range from activity-based travel choice simulators to search engines supporting personalized travel planners – in general, whenever ‘best guesses’ on mode choice patterns have to be made quickly on large amounts of data prejudicing the possibility of setting up a statistical model.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1080/03081060.2018.1541280 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:transp:v:42:y:2019:i:1:p:23-36

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GTPT20

DOI: 10.1080/03081060.2018.1541280

Access Statistics for this article

Transportation Planning and Technology is currently edited by Dr. David Gillingwater

More articles in Transportation Planning and Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().