With increasing adoption of Internet of Things (IoT) across the transportation sector, there is a growing need for developing algorithms for analyzing data streams. Due to dynamic operating environment conditions in the transportation domain, the nature of the data streams frequently change and static predictive models are often not successful when dealing with, non-stationary data streams. Further, labelled data is often unavailable or is costly to acquire in real time. Thus, effective algorithms for such problems would aim to maximize accuracy while minimizing the labelled data requirements. In this paper, we propose a new algorithm namely, the Optimal Transport based Drift Detection (OTDD) algorithm, that aims to address the accuracy-labeling requirement trade-off. Experiments on artificial and real-life data sets from the transportation domain demonstrate that the OTDD algorithm performs better than some of the widely used competing algorithms in addressing the accuracy-labeling requirement trade-off.