  Detailed understanding of multi-modal mobility patterns within urban areas is
crucial for public infrastructure planning, transportation management, and
designing public transport (PT) services centred on users' needs. Yet, even
with the rise of ubiquitous computing, sensing urban mobility patterns in a
timely fashion remains a challenge. Traditional data sources fail to fully
capture door-to-door trajectories and rely on a set of models and assumptions
to fill their gaps. This study focuses on a new type of data source that is
collected through the mobile ticketing app of HSL, the local PT operator of the
Helsinki capital region. HSL's dataset called TravelSense, records anonymized
travelers' movements within the Helsinki region by means of Bluetooth beacons,
mobile phone GPS, and phone OS activity detection. In this study, TravelSense
dataset is processed and analyzed to reveal spatio-temporal mobility patterns
as part of investigating its potentials in mobility sensing efforts. The
representativeness of the dataset is validated with two external data sources -
mobile phone trip data (for demand patterns) and travel survey data (for modal
share). Finally, practical perspectives that this dataset can yield are
presented through a preliminary analysis of PT transfers in multimodal trips
within the study area.
