Effective classification of ground transportation modes for urban data mining in smart cities

Abstract

The increasing amount of digital data in urban research has drawn attention in urban data mining. In urban research (e.g., travel studies in urban areas), researchers who conduct paper-based or telephone-based travel surveys often collect biased and inaccurate data about movements of their participants. Although the use of global positioning system (GPS) trackers in travel studies improves the accuracy of exact participant trip tracking, the challenge of labelling trip purpose and transportation mode still persists. The automation of such a task would be beneficial to travel studies and other applications that rely on contextual knowledge (e.g., current travel mode of a person). In this paper, we focus on transportation mode classification. In particular, we develop a system that improves classification accuracy of ground transportation modes (e.g., bus, car, bike, or walk). When compared with related works, our system increases the classification accuracy by uniquely using GPS and accelerometer data together with a window history queue (which uses previously encountered data). Evaluation results show that our system achieves a high classification accuracy.