Cecchetti, G, Ruscelli, AL, Ulianov, C et al. (7 more authors) (2023) Toward new generation railway Traffic Management Systems: the contribution of the OPTIMA project. Transportation Research Procedia, 72. pp. 3166-3173. ISSN 2352-1465
Abstract
Legacy Traffic Management Systems (TMS) lack interoperable data structures and standardized communication interfaces. The H2020 Shift2Rail OPTIMA project is developing a demonstrator of a communication platform for a new generation of TMS with advanced functionalities for improving the efficiency, effectiveness, and resilience of railway operations, as well as improved interoperability, modularity, and scalability. In this paper, this platform is described, along with its main components that provide seamless access to persistent and real-time data from heterogeneous data sources. A Common Data Model supports the automated data exchange process by means of a fully standardized structure for representing the data. Furthermore, the testing and validation of the OPTIMA platform and of connected TMS services when it is set up as a testing environment are illustrated.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0). |
Keywords: | Railway Traffic Management Systems; Integration Layer; Common Data Model; Technological Demonstrator; Integration of networks and services |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Jun 2023 08:03 |
Last Modified: | 18 Jan 2024 17:13 |
Published Version: | https://www.sciencedirect.com/science/article/pii/... |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.trpro.2023.11.882 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200148 |
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