TISIS : Trajectory Indexing for SImilarity Search

TL;DR

This paper introduces TISIS, an efficient trajectory indexing method for similarity search that leverages POI sharing and order, with TISIS* extending this to include semantic POI similarities via embeddings, significantly improving retrieval performance.

Contribution

The paper presents TISIS, a novel trajectory indexing approach for fast similarity search, and TISIS*, which incorporates POI embeddings for enhanced semantic trajectory retrieval.

Findings

TISIS outperforms baseline LCSS-based methods in efficiency.

TISIS* effectively captures semantic similarities between POIs.

Experimental results show significant performance improvements.

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.