Variational embedding of a hidden Markov model to generate human activity sequences

Although human trajectory data that are collected passively from location-based services (LBS) are regarded as a substitute for household travel surveys that entail a larger cost, the reality is that the data cannot be utilized directly for transportation planning and policy making without imputing missing qualitative information. Deep learning technologies have been widely used to infer the hidden features of passively collected mobile data. A deep neural network, however, is so deterministic that the probabilistic aspect of activity inference cannot be accommodated. In the present study, a stochastic approach (variational autoencoder-hidden Markov model (VAE-HMM) was devised to generate human activity chains by incorporating a VAE with a HMM. Whereas an original HMM clusters data in the observational space, the proposed approach conducts clustering in a latent space with a smaller dimension. The VAE contributes by both reducing the input dimensionality and by sidestepping the overfit to sample data. The variational inference (VI) method was used to estimate the parameters of VAE-HMM within a Bayesian framework. Data drawn from spatio-temporal, demographic, socio-economic, and individual-specific sources were chosen as input variables to feed the model. The VAE-HMM can be trained in either a supervised or an unsupervised manner.

• Record URL:
  https://doi.org/10.1016/j.trc.2021.103347
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X21003508
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Jeong, Seungyun
  • Kang, Yeseul
  • Lee, Jincheol
  • Sohn, Keemin
• Publication Date: 2021-10

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; Maps; References; Tables;
• Pagination: 103347
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 131
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Activity choices; Data collection; Data mining; Machine learning; Markov chains; Mathematical models; Stochastic processes; Trajectory; Travel behavior
• Subject Areas: Data and Information Technology; Passenger Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01785278
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 22 2021 9:21AM