{"paper":{"title":"Macroscopic Activity-Based Modeling of Urban Active Mobility","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"A macroscopic model infers urban traveler subpopulation sizes from aggregated sensor counts via attendance functions and Poisson maximum likelihood.","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Adrien Marion, Florian Patout, Romain Aza\\\"is","submitted_at":"2026-05-13T16:19:21Z","abstract_excerpt":"This paper develops a macroscopic, activity-based model of urban active mobility using nonintrusive sensor data. It introduces attendance functions to describe spatio-temporal travel patterns between activities and formulates the disaggregation of aggregated counts as a statistical inference problem. Counts are modeled as Poisson variables, and unknown subpopulation sizes are estimated via maximum likelihood, with theoretical guarantees and an efficient EM algorithm for computation. Grounded in a microscopic stochastic model, the framework offers a scalable and privacy-preserving approach to a"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Grounded in a microscopic stochastic model, the framework offers a scalable and privacy-preserving approach to analyzing urban soft mobility dynamics.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the newly introduced attendance functions accurately capture real spatio-temporal travel patterns between activities and that the Poisson model plus maximum-likelihood estimation can reliably recover subpopulation sizes from aggregated counts.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A macroscopic activity-based model estimates urban active mobility from aggregated non-intrusive sensor counts by introducing attendance functions, modeling data as Poisson, and using maximum likelihood with an EM algorithm.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A macroscopic model infers urban traveler subpopulation sizes from aggregated sensor counts via attendance functions and Poisson maximum likelihood.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"582cbfe2e4d07026d7cf014e79bf20e4de82fffe1df9c031a3b8fb865fc69f92"},"source":{"id":"2605.13742","kind":"arxiv","version":1},"verdict":{"id":"49ea3ca2-faf1-4bb7-839f-2c70ebfa501f","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T17:38:45.617260Z","strongest_claim":"Grounded in a microscopic stochastic model, the framework offers a scalable and privacy-preserving approach to analyzing urban soft mobility dynamics.","one_line_summary":"A macroscopic activity-based model estimates urban active mobility from aggregated non-intrusive sensor counts by introducing attendance functions, modeling data as Poisson, and using maximum likelihood with an EM algorithm.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the newly introduced attendance functions accurately capture real spatio-temporal travel patterns between activities and that the Poisson model plus maximum-likelihood estimation can reliably recover subpopulation sizes from aggregated counts.","pith_extraction_headline":"A macroscopic model infers urban traveler subpopulation sizes from aggregated sensor counts via attendance functions and Poisson maximum likelihood."},"references":{"count":38,"sample":[{"doi":"","year":2019,"title":"Akande, Adeoluwa and Cabral, Pedro and Gomes, Paulo and Casteleyn, Sven , journal=. 2019 , publisher=","work_id":"b7549921-73ef-41e3-8cff-4ba9c68764c5","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2008,"title":"Banister, David , journal=. 2008 , publisher=","work_id":"8752bcc0-df7a-4a7b-95de-60bd509f09a3","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Barbarossa, Luca , journal=. 2020 , publisher=","work_id":"42d3294f-2ae4-40b9-9ab1-567083bdfae8","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2015,"title":"Bonnel, Patrick and Hombourger, Etienne and Olteanu-Raimond, Ana-Maria and Smoreda, Zbigniew , journal=. 2015 , publisher=","work_id":"04f7b81e-dbad-4db9-b783-8b937e4ab4ce","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2013,"title":"Chandra, Satish and Bharti, Anish Kumar , journal=. 2013 , publisher=","work_id":"49680d80-474e-452a-8cb0-99a7da7d50a8","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":38,"snapshot_sha256":"ce32a814e4263baec10f2c0dccf41e07c31552be670129901b9518a45e53886d","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}