Use of acceleration data for transportation mode prediction

Muhammad Shafique () and Eiji Hato

Transportation, 2015, vol. 42, issue 1, 163-188

Abstract: Most smartphones today are equipped with an accelerometer, in addition to other sensors. Any data recorded by the accelerometer can be successfully utilised to determine the mode of transportation in use, which will provide an alternative to conventional household travel surveys and make it possible to implement customer-oriented advertising programmes. In this study, a comparison is made between changes in pre-processing, selection methods for generating training data, and classifiers, using the accelerometer data collected from three cities in Japan. The classifiers used were support vector machines (SVM), adaptive boosting (AdaBoost), decision tree and random forests. The results of this exercise suggest that using a 125-point moving average during pre-processing and selecting training data proportionally for all modes will maximise prediction accuracy. Moreover, random forests outperformed all other classifiers by yielding an overall prediction accuracy of 99.8 % for all three cities. Copyright The Author(s) 2015

Keywords: AdaBoost; SVM; Random forest; Decision tree; Transportation Mode (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11116-014-9541-6

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