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Dassanayake, User mobility modeling and characteriza- tion of mobility patterns, IEEE Journal on Selected Areas in Commu- nications 15 (7) (1997) 1239–1252.doi:10.1109/49.622908","work_id":"73773666-af84-401c-ad33-f1f216e32d0a","year":1997}],"snapshot_sha256":"bee62630c6dc77ccbea0914b7d352c5f64e08dd583dcaaa79072a939e45a093f"},"source":{"id":"2605.14540","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T01:31:11.356123Z","id":"ef6397ad-2ade-401c-ad83-683d2aa22fac","model_set":{"reader":"grok-4.3"},"one_line_summary":"Hierarchical clustering of Wi-Fi access points yields user mobility models with transition matrices and time vectors that show lower complexity than flat campus-wide models on real connection logs.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Wi-Fi access logs can be grouped hierarchically by location to build lower-complexity user mobility models that match observed transition patterns.","strongest_claim":"From the analysis of the mean square error of the results, we determined that the proposed method obtains good results for the transition matrices, but that the time vector definition should be improved. 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