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Analysis of Railyard Congestion and Departure Delay Relationship: a Case Study from Swedish Railways
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. (Train traffic and logistics)ORCID iD: 0000-0002-4945-3663
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. (Train traffic and logistics)ORCID iD: 0000-0003-1597-6738
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. (Train traffic and logistics)ORCID iD: 0000-0001-5269-4356
2021 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

In this paper we propose a macroscopic model framework for departure delay prediction from railyards. The railyard is a large area comprising three sub-yards (arrival, classification, departure). In fact, timely operation at railyard is dependent on coordinated operations in these sub-yards. More importantly, punctual functioning of railyards is crucial for increasing competitiveness of rail freight services throughout the network. Despite previous models, we considered railyard congestion at the arrival yard, time availability of each wagon at the classification yard, and time availability of locomotive at the departure yard. The core part of this paper analyzes the effect of congestion at arrival yard on departure delays. Punctuality data from two Swedish railyards for a seven-year period is used. The congestion is defined as the number of arriving trains three hours before each departure. The results showed that the highest number of delayed departures occur at congestion levels of 4-10, while correlation coefficient is around zero. Analysing the whole dataset reveals that these congestion levels are common for all departures not just the delayed ones. Therefore, we conclude that as three sub-yards are interrelated, a comprehensive definition of congestion at railyard level is required. An elaborate definition of congestion can make it a proper predictor for further delay prediction models.

Place, publisher, year, edition, pages
2021.
Keywords [en]
Departure Delay Prediction, Congestion, Railyards, FR8HUB, Shift2Rail
National Category
Transport Systems and Logistics
Research subject
Transport Science; Transport Science, Transport Systems; Järnvägsgruppen - Effektiva tågsystem för godstrafik; Järnvägsgruppen - Kapacitet
Identifiers
URN: urn:nbn:se:kth:diva-284668OAI: oai:DiVA.org:kth-284668DiVA, id: diva2:1485317
Conference
hEART 2020 : 9th Symposium of the European Association for Research in Transportation, Lyon, France
Projects
Shift2RailFR8HUB
Funder
Swedish Transport Administration
Note

QC 20201130

Available from: 2020-11-02 Created: 2020-11-02 Last updated: 2022-06-25Bibliographically approved
In thesis
1. Applying Data Analytics to Freight Train Delays in Shunting Yards
Open this publication in new window or tab >>Applying Data Analytics to Freight Train Delays in Shunting Yards
2020 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The European Commission has foreseen a modal share of 30% by 2030 for rail freight transport. To achieve this increase in the modal share, enhanced reliability of rail freight services is required. Optimal functioning of shunting yards is one of the areas that can improve this reliability. Shunting yards are large areas allocated to reassemble freight trains for dispatching to new destinations. Their productivity has a direct impact on the overall performance of a rail freight network. Therefore, analysing and modelling of departure deviations from shunting yards are required to enhance the interactions between shunting yards and the network; this thesis contributes to this gap. Paper I investigates the probability and temporal distribution of departure deviations using a large data set comprising 250,000 departures over seven years from two main shunting yards (Malmö and Hallsberg) in Sweden. The probability distribution of departure deviations is found comparing four main distributions including the exponential, the log-normal, the gamma, and the Weibull according to the maximum likelihood estimates and the results of the Anderson-Darling goodness of fit test.  The log-normal and the gamma are shown the best fits for departure deviations: the former on delays, and the latter on early departures. In the temporal delay distribution, the weekly and monthly, but not yearly delayed departures are positively correlated with the network usage. However, for hourly delayed departures, a shunting yard involved with international traffic does not show any correlation between delayed departures and the network usage, whereas a domestic shunting yard shows a significant negative correlation between these two parameters.  The findings obtained from this thesis contribute to a better understanding of departure deviations from shunting yards, and can be applied in enhancing the operations and capacity utilization of shunting yards in future models. Papers II and III analyse the relationship between congestion in the arrival yard and departure delays using the same data set as paper I.  According to previous research, congestion plays an important role in shunting yard delays. With defining congestion as the number of arriving trains before departure time, paper II analyses this relationship limiting the arrivals and departures between the two shunting yards considering varying time periods before departure,whereas Paper III elaborates the analysis by defining congestion level in a fixed period of time before departure time including all arrivals and departures. Considering the data set used in the analysis, the results show that there is no significant relationship between the congestion in the arrival yard and departure delays of trains. It is possible that congestion may not impact the departure delays of trains, but it may impact the departure delays of wagons due to missed wagon connection or increasing wagon idle time, which can be explored with the availability of wagon connection data.  Additionally, future elaboration of congestion definition, covering congestion at the shunting yard level, may lead to further improved analyses.

Abstract [sv]

Europeiska kommissionen har förutspått en markansandel på 30% framtill 2030 för järnvägstransporter av gods. För att uppnå denna ökning krävsökad tillförlitlighet hos järnvägstransporttjänster. Rangergodsbangårdars optimalafunktion är ett av de områden som kan förbättra denna tillförlitlighet.Rangergodsbangårdar stora områden som är avsedda för att koppla ihopgodståg för sändning till nya destinationer. Deras produktivitet har en direktinverkan på järnvägsnätets totala prestanda. Därför krävs analys och modelleringav avvikelser från dessa noder för att förbättra interaktionen mellanrangergodsbangårdar och järnvägsnätet.

I papper I undersöks sannolikheten och den tidsmässiga fördelningen avavvikelser med hjälp av en stor datamängd som omfattar 250 000 avgångaröver sju år från två rangergodsbangårdar (Malmö och Hallsberg) i Sverige.Sannolikhetsdistributioner av avvikelser jämförs med fyra huvuddistributioner,exponentiell, log-normal, gamma och Weibull enligt de maximalasannolikhetsuppskattningarna och resultaten av Anderson-Darling godhetav passningstest. Log-normal och gamma visar sig passa bäst för avvikelser:den förstnämnda vid förseningar och den senare vid tidiga avgångar. I dentidsmässiga fördröjningsfördelningen är de veckovisa och månatliga men inteårliga försenade avgångarna positivt korrelerade med järnvägsnätets nyttjandegrad.För försenade avgångar per timme visar dock en rangergodsbangårdsom är inblandad i internationell trafik ingen korrelation mellan försenadeavgångar och järnvägsnätets nyttjandegrad, medan en inhemsk rangergodsbangårdvisaren signifikant negativ korrelation mellan dessa två parametrar.Resultaten från denna avhandling bidrar till en bättre förståelse av avvikelserfrån rangergodsbangårdar och kan användas för att förbättra drift och kapacitetsutnyttjandeav rangergodsbangårdar växelplatser i framtida modeller.

Papper II och III analyserar förhållandet mellan trängsel i ankomstgårdenoch avgångsförseningar med hjälp av samma datamängd som i papperI. Enligt tidigare analyser spelar trängsel en viktig roll vid förseningar förrangergodsbangårdar. Trängsel definieras som antalet ankommande tåg föreavgångstid och papper II analyserar detta förhållande som begränsar ankomsteroch avgångar mellan de två rangergodsbangårdar med beaktande av olikatidsperioder före avgång, medan papper III utvecklar analysen genom attdefiniera trängselnivån under en fast tidsperiod före avgångstid inklusive allaankomster och avgångar. Med tanke på datamängden som användes i analysenvisar resultaten att det inte finns något signifikant samband mellan trängselni ankomstgården och tågens förseningar. Det är möjligt att trängsel kanskeinte påverkar tågens avgångsfördröjningar, men det kan påverka vagnarnasavgångsfördröjningar på grund av missad vagnanslutning eller öka vagnenstomgångstid, vilket kan undersökas med vid tillgång av vagnanslutningsdata.Dessutom kan framtida vidareutveckling av definitionen av trängsel som påen detaljerad nivå täcker rangergodsbangårdars alla delar, leda till ytterligareförbättrade analyser.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2020. p. 26
Series
TRITA-ABE-DLT ; 2033
Keywords
Shunting yards, delay prediction, congestion, delay probability distributions, temporal delay distributions, Rangergodsbangårdarna, förseningsprediktion, trängsel, fördröjning av sannolikhetsdistributioner, tidsfördröjningsdistributioner
National Category
Transport Systems and Logistics
Research subject
Transport Science, Transport Systems
Identifiers
urn:nbn:se:kth:diva-284672 (URN)978-91-7873-690-4 (ISBN)
Presentation
2020-11-30, Via Zoom https://kth-se.zoom.us/j/64405987038, Du som saknar dator/datorvana kan kontakta behzad.kordnejad@abe.kth.se, If you lack a computer or computer skills, please contact behzad.kordnejad@abe.kth.se, Stockholm, 15:00 (English)
Opponent
Supervisors
Projects
Shift2RailFR8HUB
Funder
Swedish Transport Administration
Note

QC 20201105

Available from: 2020-11-05 Created: 2020-11-02 Last updated: 2022-06-25Bibliographically approved

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Minbashi, NiloofarBohlin, MarkusKordnejad, Behzad

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