Punctuality modelling in the railway: comparative analysis of network simulation and statistical approaches on a Norwegian railway line

In this study, models have been developed to predict punctuality for railway sections. Two models have been developed and tested on the Dovre Line (Dovrebanen). Simplifications have been made, and only passenger trains run the entire section between Trondheim and Oslo. The first model that has been...

Descripción completa

Detalles Bibliográficos
Autor: Rodríguez Estrella, Josep
Tipo de recurso: tesis de maestría
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/428228
Acceso en línea:https://hdl.handle.net/2117/428228
Access Level:acceso abierto
Palabra clave:Railroads
Ferrocarrils
Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Infraestructures i transport ferroviari
id ES_e50020d077c4a55c4e262fd0e33802db
oai_identifier_str oai:upcommons.upc.edu:2117/428228
network_acronym_str ES
network_name_str España
repository_id_str
spelling Punctuality modelling in the railway: comparative analysis of network simulation and statistical approaches on a Norwegian railway lineRodríguez Estrella, JosepRailroadsFerrocarrilsÀrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Infraestructures i transport ferroviariIn this study, models have been developed to predict punctuality for railway sections. Two models have been developed and tested on the Dovre Line (Dovrebanen). Simplifications have been made, and only passenger trains run the entire section between Trondheim and Oslo. The first model that has been developed is a network model where train performance for a day is simulated. The model is programmed in Python. The simulation is based on distances and speed profiles between stations where trains can cross. Rules have been added for how crossings can be changed in the event of delays. The model is calibrated so that in a normal situation, the simulation will ensure that trains follow the timetable. To analyse the consequences of errors in the infrastructure and other conditions that can affect train performance, it is possible to analyse the effects of different scenarios such as errors that stop traffic, errors that result in temporary slow running and conditions that mean that slow running has been introduced over longer periods of time. When the model is run for such a scenario, or a combination of scenarios, one can see the effect this will have on train performance and the model calculates the associated delays. Delays will often propagate throughout the day, so that it is also possible to look at consequential delays. With such a model, it is possible to assess the consequences of errors and defects in the infrastructure, which can then be used to prioritize maintenance and renewal. A statistical model has also been developed. This model is based on minute delays registered in Bane NOR’s punctuality database. In this database, a reason code is also specified behind each delay. The most common delays are linked to reason codes that represent infrastructure problems, weather conditions and cascading effects from other delayed trains. By comparing both approaches, we see that the simulation model is able to reproduce and explain the observed delays. The analysis of specific scenarios can also identify limitations in the current timetable, including tight timetables and limited possibilities to get back on track after errors have been corrected.OutgoingUniversitat Politècnica de CatalunyaPérez González, Juan Jesús20252025-01-0120252025-04-22master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/428228reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4282282026-05-27T15:37:01Z
dc.title.none.fl_str_mv Punctuality modelling in the railway: comparative analysis of network simulation and statistical approaches on a Norwegian railway line
title Punctuality modelling in the railway: comparative analysis of network simulation and statistical approaches on a Norwegian railway line
spellingShingle Punctuality modelling in the railway: comparative analysis of network simulation and statistical approaches on a Norwegian railway line
Rodríguez Estrella, Josep
Railroads
Ferrocarrils
Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Infraestructures i transport ferroviari
title_short Punctuality modelling in the railway: comparative analysis of network simulation and statistical approaches on a Norwegian railway line
title_full Punctuality modelling in the railway: comparative analysis of network simulation and statistical approaches on a Norwegian railway line
title_fullStr Punctuality modelling in the railway: comparative analysis of network simulation and statistical approaches on a Norwegian railway line
title_full_unstemmed Punctuality modelling in the railway: comparative analysis of network simulation and statistical approaches on a Norwegian railway line
title_sort Punctuality modelling in the railway: comparative analysis of network simulation and statistical approaches on a Norwegian railway line
dc.creator.none.fl_str_mv Rodríguez Estrella, Josep
author Rodríguez Estrella, Josep
author_facet Rodríguez Estrella, Josep
author_role author
dc.contributor.none.fl_str_mv Pérez González, Juan Jesús
dc.subject.none.fl_str_mv Railroads
Ferrocarrils
Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Infraestructures i transport ferroviari
topic Railroads
Ferrocarrils
Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Infraestructures i transport ferroviari
description In this study, models have been developed to predict punctuality for railway sections. Two models have been developed and tested on the Dovre Line (Dovrebanen). Simplifications have been made, and only passenger trains run the entire section between Trondheim and Oslo. The first model that has been developed is a network model where train performance for a day is simulated. The model is programmed in Python. The simulation is based on distances and speed profiles between stations where trains can cross. Rules have been added for how crossings can be changed in the event of delays. The model is calibrated so that in a normal situation, the simulation will ensure that trains follow the timetable. To analyse the consequences of errors in the infrastructure and other conditions that can affect train performance, it is possible to analyse the effects of different scenarios such as errors that stop traffic, errors that result in temporary slow running and conditions that mean that slow running has been introduced over longer periods of time. When the model is run for such a scenario, or a combination of scenarios, one can see the effect this will have on train performance and the model calculates the associated delays. Delays will often propagate throughout the day, so that it is also possible to look at consequential delays. With such a model, it is possible to assess the consequences of errors and defects in the infrastructure, which can then be used to prioritize maintenance and renewal. A statistical model has also been developed. This model is based on minute delays registered in Bane NOR’s punctuality database. In this database, a reason code is also specified behind each delay. The most common delays are linked to reason codes that represent infrastructure problems, weather conditions and cascading effects from other delayed trains. By comparing both approaches, we see that the simulation model is able to reproduce and explain the observed delays. The analysis of specific scenarios can also identify limitations in the current timetable, including tight timetables and limited possibilities to get back on track after errors have been corrected.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-01-01
2025
2025-04-22
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/428228
url https://hdl.handle.net/2117/428228
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422640985276416
score 15.81155

Similares en LA Referencia