Activity type detection of mobile phone data based on self-training: Application of the teacher–student cycling model

Incorporating mobile phone data, known for its high spatial and temporal resolution and extensive population coverage, into Activity-Based Models (ABM) for understanding individual travel and activity behaviors is a current research hotspot. However, applying them in ABM building is not straightforward because they miss key information — activity type. In this paper, the authors present an activity types detection method named the Teacher–Student Cycling model based on a self-training framework. The authors' model can combine travel survey data with extensive prior knowledge and mobile phone data. The authors introduce two different resolutions of mobile phone datasets to test the model performance. The authors' results show that their proposed model can achieve good performance on datasets of different resolutions. The authors' model, one of the semi-supervised learning models that uses a mixture of labeled and unlabeled knowledge performs better than the supervised learning model that uses labeled knowledge alone. In addition, the authors' model improves the overall detection accuracy by 7% over the second-best semi-supervised learning model and improves the detection accuracy of secondary activities by up to 17%. The authors' model can be valuable in generating daily activity schedules for agent-based models.

• Record URL:
  • https://doi.org/10.1016/j.trc.2024.104550
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X24000718
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Gao, Lei
  • 0000-0001-6210-9368
  • Huang, Haozhe
  • 0000-0003-1675-2170
  • Ye, Jianhong
  • Wang, Daoge
  • 0000-0002-9920-1093
• Publication Date: 2024-4

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References;
• Pagination: 104550
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 161
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Bicycling; Data mining; Machine learning; Mobile telephones; Mode choice; Transportation planning
• Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; Planning and Forecasting;

Filing Info

• Accession Number: 01915606
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 18 2024 5:07PM