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

Tom Thomas (Corresponding Author), Karst T. Geurs, Johan Koolwaaij, Marcel E. Bijlsma

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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.
Original languageEnglish
Pages (from-to)143-161
Number of pages19
JournalJournal of Urban Technology
Volume25
Issue number2
Early online date24 May 2018
DOIs
Publication statusPublished - 2018

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travel
arrival time
Netherlands
registration
automatic detection
monitoring
trend
evidence
detection
rate

Keywords

  • UT-Hybrid-D

Cite this

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title = "Automatic Trip Detection with the Dutch Mobile Mobility Panel: Towards Reliable Multiple-Week Trip Registration for Large Samples",
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.",
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Automatic Trip Detection with the Dutch Mobile Mobility Panel: Towards Reliable Multiple-Week Trip Registration for Large Samples. / Thomas, Tom (Corresponding Author); Geurs, Karst T.; Koolwaaij, Johan; Bijlsma, Marcel E.

In: Journal of Urban Technology, Vol. 25, No. 2, 2018, p. 143-161.

Research output: Contribution to journalArticleAcademicpeer-review

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N2 - 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.

AB - 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.

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