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In the forthcoming decades, railway transport is expected to face a signifcant growth of traffic flows which will mostly have to be accommodated over the existing infrastructures. An increase of capacity utilization is needed to avoid reduction of reliability and punctuality of transport services. Furthermore, energy-efficient operations and multimodality opportunities are also topics of growing interest. Intelligent railway operations management requires accurate modelling and simulation of train and pedestrian traffic fows and optimal management of key decisions at strategic, tactical, and operational levels. This special issue focuses on advanced mathematical modelling and optimal control in the railway domain and related multimodal transport networks. We aim to identify new methods for improving the efectiveness and efciency of railway operations, including the development of advanced algorithmic techniques for timetabling, capacity management, infrastructure management, and trafc and passengers fow management.
 
In the forthcoming decades, railway transport is expected to face a signifcant growth of traffic flows which will mostly have to be accommodated over the existing infrastructures. An increase of capacity utilization is needed to avoid reduction of reliability and punctuality of transport services. Furthermore, energy-efficient operations and multimodality opportunities are also topics of growing interest. Intelligent railway operations management requires accurate modelling and simulation of train and pedestrian traffic fows and optimal management of key decisions at strategic, tactical, and operational levels. This special issue focuses on advanced mathematical modelling and optimal control in the railway domain and related multimodal transport networks. We aim to identify new methods for improving the efectiveness and efciency of railway operations, including the development of advanced algorithmic techniques for timetabling, capacity management, infrastructure management, and trafc and passengers fow management.
  
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DOIS: 10.1155/2018/4896748 10.3929/ethz-b-000281197
 
DOIS: 10.1155/2018/4896748 10.3929/ethz-b-000281197

Latest revision as of 16:27, 21 January 2021

Abstract

In the forthcoming decades, railway transport is expected to face a signifcant growth of traffic flows which will mostly have to be accommodated over the existing infrastructures. An increase of capacity utilization is needed to avoid reduction of reliability and punctuality of transport services. Furthermore, energy-efficient operations and multimodality opportunities are also topics of growing interest. Intelligent railway operations management requires accurate modelling and simulation of train and pedestrian traffic fows and optimal management of key decisions at strategic, tactical, and operational levels. This special issue focuses on advanced mathematical modelling and optimal control in the railway domain and related multimodal transport networks. We aim to identify new methods for improving the efectiveness and efciency of railway operations, including the development of advanced algorithmic techniques for timetabling, capacity management, infrastructure management, and trafc and passengers fow management.

Document type: Article

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Original document

The different versions of the original document can be found in:

http://downloads.hindawi.com/journals/jat/2018/4896748.xml,
http://dx.doi.org/10.1155/2018/4896748 under the license http://creativecommons.org/licenses/by/4.0/
https://doaj.org/toc/0197-6729,
https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/
https://discovery.ucl.ac.uk/id/eprint/10056770/1/4896748.pdf
https://hal.archives-ouvertes.fr/hal-02485859/document,
https://hal.archives-ouvertes.fr/hal-02485859/file/doc00031343.pdf
http://downloads.hindawi.com/journals/jat/2018/4896748.pdf,
https://iris.uniroma3.it/handle/11590/355747,
https://discovery.ucl.ac.uk/10056770,
http://discovery.ucl.ac.uk/10056770,
https://academic.microsoft.com/#/detail/2886659502



DOIS: 10.1155/2018/4896748 10.3929/ethz-b-000281197

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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.1155/2018/4896748
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