A visual segmentation method for temporal smart card data
Abstract
In many cities, worldwide public transit companies use smart card system to manage fare collection. Analysis of this acquisitive information provides a comprehensive insight of user's influence in the interactive public transit network. In this regard, analysis of temporal data, describing the time of entering to the public transit network is considered as the most substantial component of the data gathered from the smart cards. Classical distance-based techniques are not always suitable to analyze this time series data. A novel projection with intuitive visual map from higher dimension into a three-dimensional clock-like space is suggested to reveal the underlying temporal pattern of public transit users. This projection retains the temporal distance between any arbitrary pair of time-stamped data with meaningful visualization. Consequently, this information is fed into a hierarchical clustering algorithm as a method of data segmentation to discover the pattern of users.
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ML is particularly relevant for deducing information from novel data, such as learning about public transit users from smart card data.
“ML is particularly relevant for deducing information from novel data-for example, learning about the behavior of public transit users from smart card data [280,515].”
From Tackling climate change with machine learning (2023) · cites this paper as [280]
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- Authors
- Ghaemi, M. S., Agard, B., Trépanier, M., & Partovi Nia, V.
- Affiliations
- HEC Montréal (CA)
- Département de Mathématiques et de Génie Industriel, École Polytechnique de Montréal, Montréal, Québec, Canada
- Centre interuniversitaire de recherche sur les réseaux d'entreprise, la logistique et le transport (CA)
- Corresponding author
- Trépanier, Martin
- Venue
- Transportmetrica A: Transport Science
- Publisher
- Informa UK Limited
- Published
- 2017-01-17
- Volume
- 13
- Issue
- 5
- Pages
- 381-404
- ISSN
- 2324-9935 (ISSN-L)
- 2324-9943
- OpenAlex
- W2562880626
- Cited by
- 42
- References
- 36
- Citations by year
- 2025: 3 · 2024: 4 · 2023: 3 · 2022: 8 · 2021: 4 · 2020: 8 · 2019: 5 · 2018: 5 · 2017: 2
- FWCI
- 4.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transportmetrica A Transport Science (publishedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada (RDCPJ-446107-12)
- Canada excellence research chair
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Keywords
- smart card data, temporal data, public transport, fare collection, data segmentation, time series visualization, dimensionality reduction, hierarchical clustering, user behavior patterns, transit ridership
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