TY - GEN
T1 - Personalized travel mode detection with smartphone sensors
AU - Su, Xing
AU - Yao, Yuan
AU - He, Qing
AU - Lu, Jie
AU - Tong, Hanghang
N1 - Funding Information:
This work is partially supported by the National Science Foundation under Grant No. IIS-1651203, IIS-1715385, IIS-1743040 and CNS-1629888, by DTRA under the grant number HDTRA1-16-0017, by Army Research Office under the contract number W911NF-16-1-0168 and gifts from Huawei and Baidu.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
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U2 - 10.1109/BigData.2017.8258065
DO - 10.1109/BigData.2017.8258065
M3 - Conference contribution
AN - SCOPUS:85047860229
T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
SP - 1341
EP - 1348
BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
A2 - Nie, Jian-Yun
A2 - Obradovic, Zoran
A2 - Suzumura, Toyotaro
A2 - Ghosh, Rumi
A2 - Nambiar, Raghunath
A2 - Wang, Chonggang
A2 - Zang, Hui
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
A2 - Kepner, Jeremy
A2 - Cuzzocrea, Alfredo
A2 - Tang, Jian
A2 - Toyoda, Masashi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Big Data, Big Data 2017
Y2 - 11 December 2017 through 14 December 2017
ER -