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- Publisher Website: 10.1016/j.trc.2020.102627
- Scopus: eid_2-s2.0-85084646532
- WOS: WOS:000539115200001
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Article: Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model
Title | Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model |
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Authors | |
Keywords | Transit smart card Topic model Spatiotemporal pattern Human mobility Activity discovery |
Issue Date | 2020 |
Citation | Transportation Research Part C: Emerging Technologies, 2020, v. 116, article no. 102627 How to Cite? |
Abstract | © 2020 Although automatically collected human travel records can accurately capture the time and location of human movements, they do not directly explain the hidden semantic structures behind the data, e.g., activity types. This work proposes a probabilistic topic model, adapted from Latent Dirichlet Allocation (LDA), to discover representative and interpretable activity categorization from individual-level spatiotemporal data in an unsupervised manner. Specifically, the activity-travel episodes of an individual user are treated as words in a document, and each topic is a distribution over space and time that corresponds to certain type of activity. The model accounts for a mixture of discrete and continuous attributes—the location, start time of day, start day of week, and duration of each activity episode. The proposed methodology is demonstrated using pseudonymized transit smart card data from London, U.K. The results show that the model can successfully distinguish the three most basic types of activities—home, work, and other. As the specified number of activity categories increases, more specific subpatterns for home and work emerge, and both the goodness of fit and predictive performance for travel behavior improve. This work makes it possible to enrich human mobility data with representative and interpretable activity patterns without relying on predefined activity categories or heuristic rules. |
Persistent Identifier | http://hdl.handle.net/10722/287031 |
ISSN | 2023 Impact Factor: 7.6 2023 SCImago Journal Rankings: 2.860 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhao, Zhan | - |
dc.contributor.author | Koutsopoulos, Haris N. | - |
dc.contributor.author | Zhao, Jinhua | - |
dc.date.accessioned | 2020-09-07T11:46:18Z | - |
dc.date.available | 2020-09-07T11:46:18Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Transportation Research Part C: Emerging Technologies, 2020, v. 116, article no. 102627 | - |
dc.identifier.issn | 0968-090X | - |
dc.identifier.uri | http://hdl.handle.net/10722/287031 | - |
dc.description.abstract | © 2020 Although automatically collected human travel records can accurately capture the time and location of human movements, they do not directly explain the hidden semantic structures behind the data, e.g., activity types. This work proposes a probabilistic topic model, adapted from Latent Dirichlet Allocation (LDA), to discover representative and interpretable activity categorization from individual-level spatiotemporal data in an unsupervised manner. Specifically, the activity-travel episodes of an individual user are treated as words in a document, and each topic is a distribution over space and time that corresponds to certain type of activity. The model accounts for a mixture of discrete and continuous attributes—the location, start time of day, start day of week, and duration of each activity episode. The proposed methodology is demonstrated using pseudonymized transit smart card data from London, U.K. The results show that the model can successfully distinguish the three most basic types of activities—home, work, and other. As the specified number of activity categories increases, more specific subpatterns for home and work emerge, and both the goodness of fit and predictive performance for travel behavior improve. This work makes it possible to enrich human mobility data with representative and interpretable activity patterns without relying on predefined activity categories or heuristic rules. | - |
dc.language | eng | - |
dc.relation.ispartof | Transportation Research Part C: Emerging Technologies | - |
dc.subject | Transit smart card | - |
dc.subject | Topic model | - |
dc.subject | Spatiotemporal pattern | - |
dc.subject | Human mobility | - |
dc.subject | Activity discovery | - |
dc.title | Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.trc.2020.102627 | - |
dc.identifier.scopus | eid_2-s2.0-85084646532 | - |
dc.identifier.volume | 116 | - |
dc.identifier.spage | article no. 102627 | - |
dc.identifier.epage | article no. 102627 | - |
dc.identifier.isi | WOS:000539115200001 | - |
dc.identifier.issnl | 0968-090X | - |