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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 preprocessing, 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 preprocessing 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.

Smartphones are becoming increasingly popular day-by-day. Modern smartphones are more than just calling devices. They incorporate a number of high-end sensors that provide many new dimensions to smartphone experience. The use of smartphones, however, can be extended from the usual telecommunication field to applications in other specialized fields including transportation. Sensors embedded in the smartphones like GPS, accelerometer and gyroscope can collect data passively, which in turn can be processed to infer the travel mode of the smartphone user. This will solve most of the shortcomings associated with conventional travel survey methods including biased response, no response, erroneous time recording, etc. The current study uses the sensors’ data collected by smartphones to extract nine features for classification. Variables including data frequency, moving window size and proportion of data to be used for training, are dealt with to achieve better results. Random forest is used to classify the smartphone data among six modes. An overall accuracy of 99.96% is achieved, with no mode less than 99.8% for data collected at 10 Hz frequency. The accuracy is observed to decrease with decrease in data frequency, but at the same time the computation time also decreases.

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