Project description
Crowd simulation software for public transit
Public transportation grapples with challenges: shifting from individual to public transit, and meeting climate goals. Station expansion is vital, but renovating existing ones proves complex and prone to errors. While combining crowd simulation with Building Information Modeling (BIM) prevents costly errors, it requires more computing power, limiting design options. The EU-funded ACSAI project employs simulation models and machine learning to create a new model. AI swiftly assesses design feasibility, saving time by early removal of non-viable processes. The project aims to seamlessly integrate this solution into crowd simulation software for a market-ready product. This company, founded by women, strives to be a role model in a male-dominated industry.
Objective
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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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.3.2 - European innovation ecosystems
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-EIE-2022-SCALEUP-02
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
80336 MUNCHEN
Germany
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
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