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Highlights • Propose a framework to find stop episodes from spatio-temporal trajectories. • Incorporate relevant real places from OpenStreetMap for place matching. • Propose a place matching algorithm that overcomes the place ambiguity issue. • Provide experimental results supporting the effectiveness of proposed method. • Provide a case study demonstrating the validity of proposed framework. Abstract This paper introduces a place-matching pattern mining approach that detects place-matching patterns from raw spatio-temporal GPS trajectories using real-world places from OpenStreetMap. The approach begins by annotating raw trajectory recordings as either stopping or moving. It then groups contiguously stopping entries into so-called stop episodes; each of which is then associated with a number of potential stop place candidates from the real-world place repository OpenStreetMap. As each stop episode may have multiple place candidates, the proposed approach uses a Hidden Markov Model to probabilistically match each sequence of stop episodes to its most likely sequence of visited real-world places. The result of this stop episode formulation and place-matching is that the original trajectories are transformed into a discrete, greatly simplified, and more semantically meaningful sequence of place visitations. This format enables the last step of our approach where frequent itemsets and sequential patterns are extracted using traditional approaches. Experimental results with real and synthetic datasets demonstrate our approach's running time performance, robustness to GPS noise, dataset compression, and matching accuracy. Additionally, a case study using human trajectories from the real-world Geolife dataset reveals many interesting and seemingly real patterns. These findings suggest the general validity and applicability of our approach as a place-matching trajectory data mining approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We present an extensible, generic, spatio-temporal trajectory simplification framework that modularises trajectory simplification into the stages of normalising, ranking, and reduction. We combine a range of ranking strategies and scoring heuristics – some from the literature and some new – into our framework modules and create a variety of spatio-temporal trajectory simplification methods. These trajectory simplification methods are experimented upon using real world and synthetic datasets, measuring running time, geometric displacement, and region-of-interest visitation. The results indicate that our proposed framework creates a number of efficient and effective spatio-temporal trajectory simplification methods. [ABSTRACT FROM PUBLISHER]