Exploratory Analysis of a Smartphone-Based Travel Survey in Singapore

Abstract

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

“…However, we have argued that the automatic trip detection itself can also show a bias, which can be corrected for by a recall survey. Earlier smartphone studies that require user validation involve filling in missing information and amending incorrectly inferred data about, for example, trip purpose and modes of travel used (e.g., Safi et al, 2016;Zhao et al, 2015), but to our knowledge, respondents could not add undetected trips. Note from Safi et al (2016) we can derive an observed trip rate of 4.4 trips per person per day (1,873 recorded trips for 424 travel days according to their Table 3) for their sample in New Zealand, which is quite comparable with our result for The Netherlands.…”

“…In addition, many smartphone apps using GPS and GSM have been developed to provide users with feedback on their travel behavior. The use of smartphones as data collection tools to supplement or replace traditional travel surveys has been the subject of several studies, in the United States (Reddy et al, 2010), Austria (Nitsche et al, 2014), Sweden (Prelipcean et al, 2014), Shanghai, China (Xiao et al 2015), Singapore (Zhao et al, 2015), and New Zealand (Safi et al, 2016). However, except the Future Mobility Sensing pilots in Singapore with almost 800 users participating for at least 14 days, existing smartphone-based studies have been limited in terms of sample sizes and the number of observed trip legs.…”

“…However, except the Future Mobility Sensing pilots in Singapore with almost 800 users participating for at least 14 days, existing smartphone-based studies have been limited in terms of sample sizes and the number of observed trip legs. |Zhao et al (2015) conclude from pilots in Singapore that smartphone-based data have a higher resolution and better accuracy than the traditional travel surveys in Singapore, and that common problems in traditional surveys-such as underreporting of trips, overestimation of travel times, and inaccuracy of locations and times-can be reduced. The size of the Dutch Mobile Mobility Panel project is thus quite unique compared to most of the aforementioned studies.…”

“…In the past, inaccurate GPS measurements and heavy energy-consuming GPS measurements have been obstacles for large-scale applications (Rieser-Schüssler and Axhausen, 2014). Recently, with increased accuracy and employment of battery-management strategies, a growing number of studies have reported successful pilot applications (e.g., Reddy et al, 2010;Nitsche et al, 2012;Geurs et al, 2015;Cottrill et al, 2013;Zhao et al, 2015;Prelipcean et al, 2014Prelipcean et al, , 2017Safi et al, 2016).…”

Multiclass Speed-Density Relationship for Pedestrian Traffic

“…For instance, the apparent types of data are (i) the type of passenger, the time to departure, the presence of luggage and walking in groups in public transport facilities, (ii) the points of pedestrians' interest in museums and the attractivity of these points, (iii) opening hours of shops and restaurants in commercial centers, (iv) concert schedules and toilets' locations in music festivals, (v) pollution and noise levels experienced by pedestrians in urban streets, etc. To collect the mentioned characteristics various recall methods may be used, including paper-based surveys distributed to individuals (Bachu et al, 2001;Kalakou et al, 2014), smartphone-based applications (Ohmori et al, 2005;Cottrill et al, 2013;Ball et al, 2014;Zhao et al, 2015), and webbased methods (Bohte and Maat, 2009).…”