Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model
Name
discovering_latent-activity_20200213.pdf
Description
Accepted version
Size
7.72 MB
Format
Adobe PDF
Checksum (MD5)
05ec82b4ca8e25129381e993aa7148bf
Author(s)
Zhao, Zhan
Zhao, Jinhua
Date Issued
July 2020
Journal
Transportation Research Part C: Emerging Technologies
Publisher
Elsevier BV
Citation
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)
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.trc.2020.102627
