EasiTMC: Transportation Mode Classification with a High Accuracy Trajectory Detection Method

Abstract

Recently, smartphones are increasingly used in situation-aware applications, e.g. determining the way of transportation when an individual is going outside. However the existing methods of transportation mode classification often suffer from high computing complexity and unsatisfactory accuracy. In this paper we present EasiTMC, a two-stage hierarchical classification framework which consists of coarse- grained and fine-grained classifiers to automatically infer transportation modes from a GPS receiver and an accelerometer. We aim to classify different forms of transportation, including non-motorized modes such as walking, running, and biking, as well as motorized modes such as bus and car rides. The primary contributions of our work include a novel segment-based partition algorithm for achieving high accuracy with low computational complexity, a feature selection method based on overlap entropy for improving the accuracy of classification and a merge-amendment algorithm for decreasing the classification error. EasiTMC is evaluated with over 200 hours of transportation traces from seven individuals. Compared with two other segmentation methods, namely the uniform distance based and the uniform time interval based methods, the proposed partition method achieved higher degree of partition accuracy with lower computing complexity. The overall accuracy in identifying ways of transportation is about 93.3% for different users.