pith:VKMBDGZY
Macroscopic Activity-Based Modeling of Urban Active Mobility
A macroscopic model infers urban traveler subpopulation sizes from aggregated sensor counts via attendance functions and Poisson maximum likelihood.
arxiv:2605.13742 v1 · 2026-05-13 · stat.ME · stat.AP
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Claims
Grounded in a microscopic stochastic model, the framework offers a scalable and privacy-preserving approach to analyzing urban soft mobility dynamics.
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.
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.
References
Receipt and verification
| First computed | 2026-05-18T02:44:16.449227Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
aa98119b389d1243733a3270fe0d975ba898da7d09a0ef8e1b0a93701ac1885c
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/VKMBDGZYTUJEG4Z2GJYP4DMXLO \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: aa98119b389d1243733a3270fe0d975ba898da7d09a0ef8e1b0a93701ac1885c
Canonical record JSON
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