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.