We propose and compare combinations of several methods for classifying transportation activity data from smartphone GPS and accelerometer sensors. We have two main objectives. First, we aim to classify our data as accurately as possible. Second, we aim to reduce the dimensionality of the data as much as possible in order to reduce the computational burden of the classification. We combine dimension reduction and classification algorithms and compare them with a metric that balances accuracy and dimensionality. In doing so, we develop a classification algorithm that accurately classifies five different modes of transportation (i.e., walking, biking, car, bus and rail) while being computationally simple enough to run on a typical smartphone. Further, we use data that required no behavioral changes from the smartphone users to collect. Our best classification model uses the random forest algorithm to achieve 96.8% accuracy.
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Acknowledgments: The authors thank the Editor and three reviewers for the insightful feedback that greatly improved this paper. This work was supported in part by the Institute for Mathematics and its Applications and in part by NSF Grant DMS-1156701.
© 2017 by the authors. Licensee MDPI, Basel, Switzerland.
- Dimension reduction
- Mode prediction