We study the problem of identifying vehicle trajectories from the sequences of noisy geospatial-temporal datasets. Nowadays we witness the accumulation of vehicle trajectory datasets in the form of the sequences of GPS points. However, in many cases the sequences of GPS points are sparse and noisy so that identifying the actual trajectories of vehicles is hard. Although there are many advanced map-matching techniques claiming to achieve high accuracy to deal with the problem, only few public datasets that come with ground truth trajectories for supporting the claims. On the other hand, some cities are releasing their bus datasets for real-time monitoring and analytics. Since buses are expected to run on predefined routes, such datasets are highly valuable for map-matching and other pattern recognition applications. Nevertheless, some buses in reality appear not following their predefined routes and behave anomalously. We propose a simple and robust technique based on the combination of map-matching, bag-of-roads, and dimensionality reduction for their route identification. Experiments on datasets of buses in the city of Rio de Janeiro confirm the high accuracy of our method.