Sensing Multi-modal Mobility Patterns: A Case Study of Helsinki using Bluetooth Beacons and a Mobile Application

Zhiren Huang, Alonso Espinosa Mireles de Villafranca, Charalampos Sipetas

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
EditorsShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Number of pages10
ISBN (Electronic)978-1-6654-8045-1
Publication statusPublished - 2022
MoE publication typeA4 Conference publication
EventIEEE International Conference on Big Data - Osaka, Japan
Duration: 17 Dec 202220 Dec 2022


ConferenceIEEE International Conference on Big Data
Abbreviated titleBigData


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