Abstract

Detecting the travel modes such as walking and driving a car is an important task for user behavior understanding as well as transportation planning and management. Existing solutions for this task mainly train a generic classifier for all users although the walking or driving behaviors may differ greatly from one user to another. In this paper, we propose to build a personalized travel mode detection method. In particular, the proposed method can be divided into two stages. First, for a given target user, it applies user similarity computation to borrow data from a set of pre-collected data for transfer learning. Second, it estimates the data distribution in feature space, and uses it to reweight the borrowed data so as to minimize the model loss with respect to the target user. Experimental evaluations on real travel data show that the proposed method outperforms the generic method and the transfer learning method with kernel mean matching in terms of prediction accuracy.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1341-1348
Number of pages8
Volume2018-January
ISBN (Electronic)9781538627143
DOIs
Publication statusPublished - Jan 12 2018
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
CountryUnited States
CityBoston
Period12/11/1712/14/17

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

Cite this

Su, X., Yao, Y., He, Q., Lu, J., & Tong, H. (2018). Personalized travel mode detection with smartphone sensors. In Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (Vol. 2018-January, pp. 1341-1348). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258065