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

Martin, Bryan D.; Addona, Vittorio; Wolfson, Julian; Adomavicius, Gediminas; Fan, Yingling

doi:10.3390/s17092058

Open AccessArticle

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

by

Bryan D. Martin

^1,*

,

Vittorio Addona

²

,

Julian Wolfson

³,

Gediminas Adomavicius

⁴ and

Yingling Fan

⁵

¹

Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, USA

²

Department of Mathematics, Statistics, and Computer Science, Macalester College, St. Paul, MN 55105-1899, USA

³

Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455-0341, USA

⁴

Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, MN 55455-0438, USA

⁵

Humphrey School of Public Affairs, University of Minnesota, MN 55455-0395, USA

^*

Author to whom correspondence should be addressed.

Sensors 2017, 17(9), 2058; https://doi.org/10.3390/s17092058

Submission received: 29 June 2017 / Revised: 29 August 2017 / Accepted: 5 September 2017 / Published: 8 September 2017

(This article belongs to the Section Physical Sensors)

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Abstract

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.

Keywords: mode prediction; movelets; dimension reduction; classification

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MDPI and ACS Style

Martin, B.D.; Addona, V.; Wolfson, J.; Adomavicius, G.; Fan, Y. Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data. Sensors 2017, 17, 2058. https://doi.org/10.3390/s17092058

AMA Style

Martin BD, Addona V, Wolfson J, Adomavicius G, Fan Y. Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data. Sensors. 2017; 17(9):2058. https://doi.org/10.3390/s17092058

Chicago/Turabian Style

Martin, Bryan D., Vittorio Addona, Julian Wolfson, Gediminas Adomavicius, and Yingling Fan. 2017. "Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data" Sensors 17, no. 9: 2058. https://doi.org/10.3390/s17092058

APA Style

Martin, B. D., Addona, V., Wolfson, J., Adomavicius, G., & Fan, Y. (2017). Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data. Sensors, 17(9), 2058. https://doi.org/10.3390/s17092058

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

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