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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]

We focus on the problem of using contiguous SPM to extract succinct, redundancy controlled patterns from large vehicle trajectories. Although there exist several techniques to reduce the contiguous sequential pattern output such as closed and max SPM, they still produce massive redundant pattern outputs when the input sequence database is sufficiently large and homogeneous — as is often the case for vehicle trajectories. Therefore, in this work we propose DC-SPAN: a distinct contiguous SPM algorithm. DC-SPAN mines a set of sequential patterns where the maximum redundancy of the pattern output is controlled by a user-specified parameter. Through various experiments using real world trajectory datasets we show DC-SPAN effectively controls the redundancy of the pattern output with trade-offs in pattern distinctness. Additionally, our experiments also indicate that DC-SPAN efficiently computes these patterns, incurring only a marginal running time cost over existing state-of-the-art contiguous SPM approaches. Lastly, due to the less redundant and more succinct pattern output we also briefly explore visualisation as a useful technique to interpret the discovered vehicle routes. [ABSTRACT FROM AUTHOR]