2022/09/04 by Zhiren Huang, Huang, Zhiren, Alonso Espinosa Mireles de Villafranca +3
Social Sciences · #Databases (cs.DB) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Networking and Internet Architecture (cs.NI) #Transportation Planning and Optimization #Urban Transport and Accessibility
paper · pdf · doi:10.48550/arxiv.2209.13537
openalex publication_date 2022/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.