App-based automatic collection of travel behaviour: A field study comparison with self-reported behaviour

Abstract

Smart phone apps hold great promise for travel behaviour research, but theirperformance relative to traditional methods is still not well understood. The aim of thisstudy is to evaluate the magnitude and direction of differences between travel behaviour from a completely automatic travel mode detection mobile app and a traditional travel behaviour survey. We present data from n = 230 participants who used the app (sense.dat) for four weeks. Participantsalso completed a one-daytravel diary and one-week retrospective account of cycling and walkingin the same period.Correspondence between app and survey varied across levels of aggregation and modalities. Overall, the app recorded substantially more km, minutes and non-zero trip days than the one-day survey, but when split up by mode this was not true for public transport. On the individual level there was atendency for the app to register modes not self-reported by the respondents for allmodes except public transport, possibly indicating that the app captures trips that theuser may have forgot or intentionally left out.For bike, car and foot, the Spearman correlations between app and survey registered (one-day)distances and durations were moderate (r > 0.5) or strong (r > 0.8) when based onobservations that were non-zero in both data sources, and moderate or weak whenbased on all observations. For one-week reports of active transport modes, app-survey correlations were lower than for the one-day data, especially for foot.