Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data.

Citation data:

Sensors (Basel, Switzerland), ISSN: 1424-8220, Vol: 17, Issue: 9, Page: 2058

Publication Year:
2017
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Repository URL:
https://digitalcommons.macalester.edu/mathfacpub/6
PMID:
28885550
DOI:
10.3390/s17092058
Author(s):
Martin, Bryan D.; Addona, Vittorio; Wolfson, Julian; Adomavicius, Gediminas; Fan, Yingling
Publisher(s):
MDPI AG
Tags:
Chemistry; Physics and Astronomy; Biochemistry, Genetics and Molecular Biology; Engineering; mode prediction; movelets; dimension reduction; classification; Remote Sensing; Theory and Algorithms
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article description
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.