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Sensors 2014, 14(11), 20843-20865; doi:10.3390/s141120843

Using Smart Phone Sensors to Detect Transportation Modes

The Institute of Remote Sensing and Digital Earth, No.20 Datun Road, Chaoyang District, Beijing 100101, China
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Received: 14 May 2014 / Revised: 25 September 2014 / Accepted: 23 October 2014 / Published: 4 November 2014
(This article belongs to the Special Issue Positioning and Tracking Sensors and Technologies in Road Transport)
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Abstract

The proliferation of mobile smart devices has led to a rapid increase of location-based services, many of which are amassing large datasets of user trajectory information. Unfortunately, current trajectory information is not yet sufficiently rich to support classification of user transportation modes. In this paper, we propose a method that employs both the Global Positioning System and accelerometer data from smart devices to classify user outdoor transportation modes. The classified modes include walking, bicycling, and motorized transport, in addition to the motionless (stationary) state, for which we provide new depth analysis. In our classification, stationary mode has two sub-modes: stay (remaining in the same place for a prolonged time period; e.g., in a parked vehicle) and wait (remaining at a location for a short period; e.g., waiting at a red traffic light). These two sub-modes present different semantics for data mining applications. We use support vector machines with parameters that are optimized for pattern recognition. In addition, we employ ant colony optimization to reduce the dimension of features and analyze their relative importance. The resulting classification system achieves an accuracy rate of 96.31% when applied to a dataset obtained from 18 mobile users. View Full-Text
Keywords: transportation mode classification; built-in sensor; smart phone; trajectory transportation mode classification; built-in sensor; smart phone; trajectory
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xia, H.; Qiao, Y.; Jian, J.; Chang, Y. Using Smart Phone Sensors to Detect Transportation Modes. Sensors 2014, 14, 20843-20865.

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