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ACSAI

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Automated Crowd Simulation with Artificial Intelligence

With the transformation of transportation from individual to public transport and the new climate goals of the European Green Deal, public transportation faces major challenges in the coming years. The number of passengers is expe... ver más

31/08/2024

ACCURATE GMBH

75K€

Presupuesto del proyecto: 75K€

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ACCURATE GMBH No se ha especificado una descripción o un objeto social para esta compañía.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-08-31

HORIZON-EIE-2022-SCALEUP-02-02: Women TechEU ExpectedOutcome:Project results are expected to contribute to the following expected outcomes:

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Duración del proyecto: 13 meses Fecha Inicio: 2023-07-04
Fecha Fin: 2024-08-31

Líder del proyecto

ACCURATE GMBH No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 75K€

Descripción del proyecto With the transformation of transportation from individual to public transport and the new climate goals of the European Green Deal, public transportation faces major challenges in the coming years. The number of passengers is expected to more than double. Stations must be quickly expanded to accommodate the increasing number of passengers. Remodeling existing stations is more complex than building new ones, is error prone and quickly lead to costs and time explosions. New methods, technologies, and processes are needed to solve this. Our project Automated Crowd Simulation with Artificial Intelligence (ACSAI) focuses on exploring and combining existing deep tech solutions into a new one. BIM in combination with crowd simulation enables planners to digitally prototype buildings at early planning stages to prevent costly mistakes. Although BIM speeds the process of data preparation, a high computational effort is needed for the execution. As a result, only a few design variants or scenarios can be analysed as of today. To overcome this issue, we combine simulation models with machine learning methods: AI Models shall be developed and trained with the help of precalculated simulation data. The trained NN can give answers at the push of a button, whether a design is compliant or not. This accelerates the process enormously since it quickly sorts out designs that are not feasible. Thus, only pre-checked design variants will be simulated for final confirmation and optimisation. The whole approach is a quantum leap in the planning process since it speeds it up and reduces the risk of planning mistakes enormously. We have tested the approach for railway stations in a PoC environment in the research project BEYOND. The next step is to implement it in our crowd simulation software into a ready-to-market product. As a small company, we cannot afford such big R&D projects on our own. And as a female-founded company we hope to be a role model in a male-dominated sector.

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