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Auteur W. Griffin |
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Building agent-based walking models by machine-learning on diverse databases of space-time trajectory samples / Paul M. Torrens in Transactions in GIS, vol 15 supplement s1 (July 2011)
[article]
Titre : Building agent-based walking models by machine-learning on diverse databases of space-time trajectory samples Type de document : Article/Communication Auteurs : Paul M. Torrens, Auteur ; W. Griffin, Auteur ; X. Li, Auteur Année de publication : 2011 Article en page(s) : pp 67 - 94 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] agent (intelligence artificielle)
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] base de données spatiotemporelles
[Termes IGN] itinéraire
[Termes IGN] milieu urbain
[Termes IGN] navigation pédestre
[Termes IGN] piéton
[Termes IGN] service fondé sur la position
[Termes IGN] trajet (mobilité)Résumé : (Auteur) We introduce a novel scheme for automatically deriving synthetic walking (locomotion) and movement (steering and avoidance) behavior in simulation from simple trajectory samples. We use a combination of observed and recorded real-world movement trajectory samples in conjunction with synthetic, agent-generated, movement as inputs to a machine-learning scheme. This scheme produces movement behavior for non-sampled scenarios in simulation, for applications that can differ widely from the original collection settings. It does this by benchmarking a simulated pedestrian's relative behavioral geography, local physical environment, and neighboring agent-pedestrians; using spatial analysis, spatial data access, classification, and clustering. The scheme then weights, trains, and tunes likely synthetic movement behavior, per-agent, per-location, per-time-step, and per-scenario. To prove its usefulness, we demonstrate the task of generating synthetic, non-sampled, agent-based pedestrian movement in simulated urban environments, where the scheme proves to be a useful substitute for traditional transition-driven methods for determining agent behavior. The potential broader applications of the scheme are numerous and include the design and delivery of location-based services, evaluation of architectures for mobile communications technologies, what-if experimentation in agent-based models with hypotheses that are informed or translated from data, and the construction of algorithms for extracting and annotating space-time paths in massive data-sets. Numéro de notice : A2011-251 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2011.01261.x Date de publication en ligne : 09/06/2011 En ligne : https://doi.org/10.1111/j.1467-9671.2011.01261.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31029
in Transactions in GIS > vol 15 supplement s1 (July 2011) . - pp 67 - 94[article]