Understanding the protection of privacy when counting subway travelers through anonymization
Open accessAbstract
Public transportation, especially in large cities, is critical for livability. Counting passengers as they travel between stations is crucial to establishing and maintaining effective transportation systems. Various information and communication technologies, such as GPS, Bluetooth, and Wi-Fi, have been used to measure people's movements automatically. Regarding public transportation applications, the automated fare collection (AFC) system has been widely adopted as a convenient method for measuring passengers, mainly because it is relatively easy to identify card owners uniquely and, as such, the movements of their card holders. However, there are serious concerns regarding privacy infringements when deploying such technologies, to the extent that Europe's General Data Protection Regulation has forbidden straightforward deployment for measuring pedestrian dynamics unless explicit consent has been provided. As a result, privacy-preservation techniques (e.g., anonymization) must be used when deploying such systems. Against this backdrop, we investigate to what extent a recently developed anonymization technique, known as detection k-anonymity, can be adapted to count public transportation travelers while preserving privacy. In the case study, we tested our methods with data from Beijing subway trips. Results show different scenarios when detection k-anonymity can be effectively applied and when it cannot. Due to the complicated relationship between the detection k-anonymity parameters, setting the proper parameter values can be difficult, leading to inaccurate results. Furthermore, through detection k-anonymity, it is possible to count travelers between two locations with high accuracy. However, counting travelers from more than two locations leads to more inaccurate results.
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Addressing challenges through preprocessing techniques, comprehensive training datasets, adherence to data privacy regulations, and strategic system design will be crucial for successful deployment and operation of these systems in public transport.
“Addressing these challenges through preprocessing techniques [102], comprehensive training datasets [103], adherence to data privacy regulations [104], and strategic system design will be crucial for the successful deployment and operation of these systems in public transport.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [104]
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- Authors
- Shafaeipour, N., Stanciu, V.-D., van Steen, M., & Wang, M.
- Affiliations
- International Institute for Geo-Information Science and Earth Observation (NL)
- University of Twente (NL)
- University of Glasgow (GB)
- Corresponding author
- Wang, Mingshu
- Venue
- Computers, Environment and Urban Systems
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 110
- Pages
- 102091
- ISSN
- 0198-9715 (ISSN-L)
- OpenAlex
- W4392798180
- Cited by
- 6
- References
- 43
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2
- FWCI
- 0.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers Environment and Urban Systems (publishedVersion, cc-by)
- University of Twente Research Information (publishedVersion, cc-by)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- Funders
- Dutch Research Council (410.19.002)
- University of Glasgow
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- privacy preservation, automatic fare collection, data anonymization, General Data Protection Regulation, public transport, Beijing subway, pedestrian dynamics
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