A probabilistic stop and move classifier for noisy GPS trajectories

Stop and move information can be used to uncover useful semantic patterns; therefore, annotating GPS trajectories as either stopping or moving is beneficial. However, the task of automatically discovering if the entity is stopping or moving is challenging due to the spatial noisiness of real-world GPS trajectories. Existing approaches classify each entry definitively as being either a stop or a move: hiding all indication that some classifications can be made with more certainty than others. Such an indication of the “goodness of classification” of each entry would allow the user to filter out certain stop classifications that appear too ambiguous for their use-case, which in a data-mining context may ultimately lead to less false patterns. In this work we propose such an approach that takes a noisy GPS trajectory as input and calculates the stop probability at each entry. Through the use of a minimum stop probability parameter our proposed approach allows the user to directly filter out any classified stops that are of an unacceptable probability for their application. Using several real-world and synthetic GPS trajectories (that we have made available) we compared the classification effectiveness, parameter sensitivity, and running time of our approach to two well-known existing approaches SMoT and CB-SMoT. Experimental results indicated the efficiency, effectiveness, and sampling rate robustness of our approach compared to the existing approaches. The results also demonstrated that the user can increase the minimum stop probability parameter to easily filter out low probability stop classifications—which equated to effectively reducing the number of false positive classifications in our ground truth experiments. Lastly, we proposed estimation heuristics for each our approaches’ parameters and empirically demonstrated the effectiveness of each heuristic using real-world trajectories. Specifically, the results revealed that even when all of the parameters were estimated the classification effectiveness of our approach was higher than existing approaches across a range of sampling rates.