IN2DREAMS - INtelligent solutions 2ward the Development of Railway Energy and Asset Management Systems in Europe
The predicted growth of transport, especially in European railway infrastructures, is expected to introduce a dramatic increase in freight and passenger services by the end of 2050. To support sustainable development of these infrastructures, novel data-driven ICT solutions are required. These will enable monitoring, analysis and exploitation of energy and asset information for the entire railway system including power grid, stations, rolling stock and infrastructure. IN2DREAMS will address these challenges through two distinct work streams: WS1, focusing on the management of energy-related data and WS2, focusing on the management of asset-related data. IN2DREAMS will develop and demonstrate a modular cloud-based open data management platform (ODM) facilitating ubiquitous support of both energy and asset services. WS1 will provide energy metering services through a dynamically reconfigurable platform offering improved reliability, ease of monitoring and on-the-fly optimisation for the entire railway system. This will include a heterogeneous secure and resilient telecommunication platform comprising both wireless and wireline technologies converging energy and telecom services. This infrastructure will interconnect a plethora of monitoring devices and end-users to the railway control centre and will include an ODM platform for data collection, aggregation and analysis, able to scale with the railway operators needs. This platform will be non-intrusive exploiting advanced signal processing and intelligent learning algorithms. Within WS2, IN2DREAMS will concentrate on defining IT solutions and methodologies for business-secure decision support in the field of data processing and analytics for railway asset management. The general aim is to study and proof the application of smart contracts in the railway ecosystems, by addressing also legal and regulatory implications, and advanced visual and rule-based data analytics, including metrics for performance assessment.
- AG Keim (Data Analysis and Visualization)
| (2020): Visual Analytics for Supporting Conflict Resolution in Large Railway Networks ONETO, Luca, ed. and others. Recent Advances in Big Data and Deep Learning : Proceedings of the INNS Big Data and Deep Learning Conference, INNSBDDL2019. Cham: Springer, 2020, pp. 206-215. Proceedings of the International Neural Networks Society. 1. ISSN 2661-8141. eISSN 2661-815X. ISBN 978-3-030-16840-7. Available under: doi: 10.1007/978-3-030-16841-4_22 |
Train operators are responsible for maintaining and following the schedule of large-scale railway transport systems. Disruptions to this schedule imply conflicts that occur when two trains are bound to use the same railway segment. It is upon the train operator to decide which train must go first to resolve the conflict. As the railway transport system is a large and complex network, the decision may have a high impact on the future schedule, further train delay, costs, and other performance indicators. Due to this complexity and the enormous amount of underlying data, machine learning models have proven to be useful. However, the automated models are not accessible to the train operators which results in a low trust in following their predictions. We propose a Visual Analytics solution for a decision support system to support the train operators in making an informed decision while providing access to the complex machine learning models. Different integrated, interactive views allow the train operator to explore the various impacts that a decision may have. Additionally, the user can compare various data-driven models which are structured by an experience-based model. We demonstrate a decision-making process in a use case highlighting how the different views are made use of by the train operator. Forschungszusammenhang (Projekte) |
| Name | Finanzierungstyp | Kategorie | Kennziffer |
|---|---|---|---|
| Europäische Union | Drittmittel | Forschungsförderprogramm | 739/17 |
| Laufzeit: | seit 31.08.2019 |