Automatic Trip Detection with the Dutch Mobile Mobility Panel: Towards Reliable Multiple-Week Trip Registration for Large Samples

Tom Thomas, Karst T. Geurs, Johan Koolwaaij and Marcel Bijlsma

Journal of Urban Technology, 2018, vol. 25, issue 2, 143-161

Abstract: This paper examines the accuracy of trip and mode choice detection of the last wave of the Dutch Mobile Mobility Panel, a large-scale three-year, smartphone-based travel survey. Departure and arrival times, origins, destinations, modes, and travel purposes were recorded during a four week period in 2015, using the MoveSmarter app for a representative sample of 615 respondents, yielding over 60 thousand trips. During the monitoring period, respondents also participated in a web-based prompted recall survey and answered additional questions. This enables a comparison between automatic detected and reported trips. Most trips were detected with no clear biases in trip length or duration, and transport modes were classified correctly for over 80 percent of these trips. There is strong evidence that smartphone-based trip detection helps to reduce underreporting of trips, which is a common phenomenon in travel surveys. In the Dutch Mobile Mobility Panel, trip rates are substantially higher than trip-diary based travel surveys in the Netherlands, in particular for business and leisure trips which are often irregular. The rate of reporting also hardly decreased during the four-week period, which is a promising result for the use of smartphones in long duration travel surveys.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1080/10630732.2018.1471874 (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:cjutxx:v:25:y:2018:i:2:p:143-161

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/cjut20

DOI: 10.1080/10630732.2018.1471874

Access Statistics for this article

Journal of Urban Technology is currently edited by Richard E. Hanley

More articles in Journal of Urban Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().