• Expression of Interest

    Digital Twin for ORD of (Trans-)National Infrastructure and Systems
    Digital Twin, Mobility, Data collection, Data storage, Data processing, Data analysis, Data visualisation, Energy, Logistics

    Digital Twins (DTs) are the newest buzzword for Information and Communication Technology (ICT) and follow the Internet of Things (IoT) as a Gartner’s hyped technology (Tao et al., 2018). While there is no consensus on the meaning of DTs (Grübel et al., 2022), they usually consist of geo-referenced data to represent relevant elements of a system for analysis and decision-making (Grieves & Vickers, 2017). However, the model simplicity–a Physical Twin and a Digital Twin (Grübel et al., 2022)–that subsumes implementation complexity is alluring across industry, governance, and research. New DTs are constantly developed (Grübel et al., 2022). Nonetheless, each DT is a unique ungeneralised artefact and there is still no open format for designing DTs and exchanging data between DTs (Roest, 2019). We are looking to develop a Open Research Data (ORD) platform to develop DTs of national level infrastructure. At the Center for Sustainable Future Mobility (CSFM), we focus on mobility. For us, DTs would describe individual or household spatial behaviour with a temporal resolution anywhere from annual to real-time data. There is a wide array of ORD practices available such as national and regional Open Data sources for mobility (e.g. NADIM in Switzerland) and ORD data processing pipelines such as Renku that could be combined into DTs. In this project, we close this gap by joining existing open standards to coordinate them as a DT open standard. For instance, together with the Swiss Data Science Center (SDSC), we will integrate the Renku platform into ORD practises in mobility research. However, a DT is not limited to mobility and my find applications in energy research, logistical operations and more. Furthermore, with appropriate standard different national DTs could be combined into transnational representations. We aim to develop open standards for such DTs to underpin future research with FAIR guidelines. The DT format allows for practices of easier communication of ideas, results, and methods as the data is represented from collection over analysis to presentation. Thus, we realize the potential of ORD practices in mobility research and beyond through the DT approach.

    Fix:

    • ETH Zürich (CSFM)

    Interested:

    • Swiss Data Science Center (ORD Group)

    We are looking for research groups that expand that are interested in developing and applying large-scale Digital Twins (roles & topics in no particular order):

    • Develop DT of mobility system for other country than Switzerland
    • Develop DT for other field than mobility
    • Develop open standard for DT that considers aspects of data collection, data storage, data processing, data analysis and data visualization
    • Develop data model for DTs that takes into consideration access restriction due to confidential data or privacy-protected data (e.g. synthetic populations)
    • Develop raw data to DT pipeline for open data sources
    • Develop storage standards for DTs to enable reuse of open data bases and addition of new data
    • Develop processing and analysis interfaces for DTs to use different analytical engines (e.g. MatSIM, ML models)
    • Develop visualization for DTs to enable researchers, policy makers and the general public to interact with data.
    Different from those already involved

    We are currently developing a Digital Twin of the Swiss Mobility System but we would like to generalize our work and find partners to strengthen ORD practices in mobility research and beyond. With this project, we would like to either focus on developing generic Digital Twins of National Mobility Systems or even Digital Twins of National Infrastructure and Systems depending on the interest of other groups. We are also open to get others involved in the standardization process as well as developing subsystems and components of the Digital Twin to offer other researchers more ORD tools and practices.

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