Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model
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 Allo...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2020
|
| Online Access: | https://hdl.handle.net/1721.1/127232 |
| _version_ | 1826195861687762944 |
|---|---|
| author | Zhao, Zhan Zhao, Jinhua |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Zhao, Zhan Zhao, Jinhua |
| author_sort | Zhao, Zhan |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:16:43Z |
| format | Article |
| id | mit-1721.1/127232 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:16:43Z |
| publishDate | 2020 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1272322022-09-26T16:58:47Z Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model Zhao, Zhan Zhao, Jinhua Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Department of Urban Studies and Planning 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. 2020-09-10T16:50:13Z 2020-09-10T16:50:13Z 2020-07 2020-02 2020-08-31T12:40:05Z Article http://purl.org/eprint/type/JournalArticle 0968-090X https://hdl.handle.net/1721.1/127232 Zhao, Zhan, Haris N. Koutsopoulosb and Jinhua Zhao. “Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model.” Transportation Research Part C: Emerging Technologies, 116 (July 2020): 102627 © 2020 The Author(s) en 10.1016/j.trc.2020.102627 Transportation Research Part C: Emerging Technologies Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV MIT web domain |
| spellingShingle | Zhao, Zhan Zhao, Jinhua 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 |
| title_full | Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model |
| title_fullStr | Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model |
| title_full_unstemmed | Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model |
| title_short | Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model |
| title_sort | discovering latent activity patterns from transit smart card data a spatiotemporal topic model |
| url | https://hdl.handle.net/1721.1/127232 |
| work_keys_str_mv | AT zhaozhan discoveringlatentactivitypatternsfromtransitsmartcarddataaspatiotemporaltopicmodel AT zhaojinhua discoveringlatentactivitypatternsfromtransitsmartcarddataaspatiotemporaltopicmodel |