TISIS : Trajectory Indexing for SImilarity Search
Sara Jarrad, Hubert Naacke, Stephane Gancarski
TL;DR
The paper tackles scalable retrieval of similar trajectories defined over sequences of POIs by leveraging LCSS-based similarity. It introduces TISIS, an exact trajectory indexing method, and TISIS*, an embedding-enhanced variant that enables contextual POI similarity using Word2Vec embeddings; both aim to reproduce LCSS results while reducing search cost. Empirical evaluation on three real datasets (Foursquare, Gowalla, YFCC) shows substantial speedups over a naive LCSS baseline, with 2P indexing providing notable gains and embeddings increasing result coverage at a modest cost. The work advances practical, large-scale trajectory similarity search by providing both exact and embedding-based indexing strategies and highlighting their performance trade-offs.
Abstract
Social media platforms enable users to share diverse types of information, including geolocation data that captures their movement patterns. Such geolocation data can be leveraged to reconstruct the trajectory of a user's visited Points of Interest (POIs). A key requirement in numerous applications is the ability to measure the similarity between such trajectories, as this facilitates the retrieval of trajectories that are similar to a given reference trajectory. This is the main focus of our work. Existing methods predominantly rely on applying a similarity function to each candidate trajectory to identify those that are sufficiently similar. However, this approach becomes computationally expensive when dealing with large-scale datasets. To mitigate this challenge, we propose TISIS, an efficient method that uses trajectory indexing to quickly find similar trajectories that share common POIs in the same order. Furthermore, to account for scenarios where POIs in trajectories may not exactly match but are contextually similar, we introduce TISIS*, a variant of TISIS that incorporates POI embeddings. This extension allows for more comprehensive retrieval of similar trajectories by considering semantic similarities between POIs, beyond mere exact matches. Extensive experimental evaluations demonstrate that the proposed approach significantly outperforms a baseline method based on the well-known Longest Common SubSequence (LCSS) algorithm, yielding substantial performance improvements across various real-world datasets.