Real-Time Travel Mode Detection with Smartphone Sensing and Machine Learning
The detection of the travel modes used and, the prediction of trip purposes, through smartphone sensors data have emerged as two research challenges in recent years. Both of these problems have been deeply investigated in isolation, while the problem of inferring mode and purpose at the same time and, more specifically, using the same preprocessing algorithm has been less explored. Also, few studies presented solutions that can execute the detection of user travel modes in real-time, and even fewer have presented the evaluation of these solutions in a realistic manner. Meanwhile, some of the previous studies claim that off-the-shelf activity recognition solutions, do not perform well in the travel mode detection task, although many of them do not present a quantitative evidence of their bad performance. Thus, in this work, we propose three techniques for real-time travel mode detection, using different combinations of smartphone sensors, and one technique for join travel mode detection and trip purpose prediction using a single preprocessing algorithm, in real-time. We empirically evaluated the proposed techniques and an off-the-shelf activity recognition solution using field tests and cross-validation experiments with private and public mobility datasets.
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