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Formation of Training and Testing Datasets, for Transportation Mode Identification

Muhammad Awais Shafique 1 and Eiji Hato 2
1. Department of Transportation Engineering & Management, University of Engineering & Technology, Lahore, Pakistan
2. Department of Civil Engineering, The University of Tokyo, Tokyo, Japan
Abstract—Recently, a lot of research is done to automatically predict the mode of transportation used by the smartphone carrier by collecting and analyzing the data from sensors like global positioning system (GPS) and accelerometer. The most popular methodology is to train a classification algorithm and then use it to classify the test data. This study provides an insight into how the training and testing datasets should be formed. A comparison is made among the two approaches i.e. randomly splitting the data from all participants into training and testing datasets, or using some participants’ data to form training dataset and rest to form testing dataset. For the first method, 50% data from 6 participants was randomly selected to train Random Forest while the rest was used to test it. For the second method, 5 participants’ data was used to train the algorithm and a different set of 5 participants’ data was used to test it. Results concluded that splitting the data causes over-estimation; therefore different datasets should be utilized for the purpose of mode identification.

Index Terms—accelerometer, classification, GPS, random forest, travel mode

Cite: Muhammad Awais Shafique and Eiji Hato, "Formation of Training and Testing Datasets, for Transportation Mode Identification," Journal of Traffic and Logistics Engineering, Vol. 3, No. 1, pp. 77-80, June 2015. doi: 10.12720/jtle.3.1.77-80
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