Detecting Similarities in Mobility Patterns

Cottone, Pietro; Ortolani, Marco; Pergola, Gabriele

Abstract

The wide spread of low-cost personal devices equipped with GPS sensors has paved the way towards the creation of customized services based on user mobility habits and able to track and assist users in everyday activities, according to their current location.

In this paper we propose a new approach to extraction and comparison of mobility models, by means of the structure inferred from positioning data. More specifically, we suggest to use concepts and methods borrowed from Algorithmic Learning Theory (ALT) and we formulate mobility models extraction in term of Grammatical Inference (GI), an inductive process able to select the best grammar consistent with the samples and to provide multi-scale generative models. Moreover, we propose a similarity measure by adapting a state-of-the-art metric originally conceived for automata.

A thorough experimental assessment was conducted on the publicly available dataset provided by the Geolife project. Results show how a structural model and similarity metric can provide a better insight on data despite its complexity.

Citation

Cottone, P., Ortolani, M., & Pergola, G. (2016, August). Detecting Similarities in Mobility Patterns. Presented at 8th European Starting AI Researcher Symposium (STAIRS 2016), The Hague, the Netherlands

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 8th European Starting AI Researcher Symposium (STAIRS 2016)
• Start Date: Aug 26, 2016
• End Date: Sep 2, 2016
• Publication Date: 2016-08
• Deposit Date: Dec 14, 2023
• Publisher: IOS Press
• Volume: 284
• Pages: 167 - 178
• Book Title: Frontiers in Artificial Intelligence and Applications
• ISBN: 978-1-61499-681-1
• DOI: https://doi.org/10.3233/978-1-61499-682-8-167
• Public URL: https://keele-repository.worktribe.com/output/668118
• Publisher URL: https://ebooks.iospress.nl/volumearticle/45052