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Application of Predictive Analytics for Shunting Yard Delays
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
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Increasing the modal share of rail freight transport is one of the main ways to achieve carbon neutrality in Europe. The perceived low reliability and predictability of rail freight services is one of the main challenges to overcome in reaching this target. Shunting yards play an important role in providing more reliable and predictable freight trains. Shunting yard departure deviations impact other trains on mixed-traffic railway networks. Predictable departures from shunting yards increase the overall predictability of freight train runs along the network.

The primary focus of this thesis is on how to apply data-driven approaches to increase the predictability of shunting yard departures. Descriptive analytics were used to provide enhanced insight into shunting yard departures, and predictive analytics were applied to develop shunting yard departure deviation prediction models. Finally, hybrid modeling was used to integrate the yard departure prediction model with other simulation models for wider application. The results from this thesis contribute to providing a deeper understanding of shunting yard departure deviations, interactions between shunting yards and the network through departure and arrival deviations, and how to model these deviations by applying data-driven approaches. These results from five published research papers are included and presented in this doctoral thesis.

Descriptive analytics methods are applied in papers I and II to explore the probability distribution of departure deviations and the impact of the network on departure delays. The results show that positive and negative departure deviations have different distributions for different shunting yards. Moreover, network usage fluctuations over shorter timespans impact departure delays, whereas no correlation is established between network impact, defined as congestion in the arrival yard, and departure delays.

Predictive analytics is applied in paper III by developing tree-based algorithms to classify the status of shunting yard departures. The departure status are imbalanced; the majority are early, and the minority are delayed. The results show that applying methods to overcome imbalanced data sets can improve the prediction of delayed departures.

The models developed in paper III are extended in papers IV and V to predict departure deviations in a combined modeling approach for two separate applications. In paper IV, a machine learning-assisted macro simulation model framework is introduced to integrate yard departure predictions into a macro simulation network model and predict the arrivals to the next yard. The results show improved prediction accuracy compared to a basic machine learning model and a baseline timetable model.

Finally, in paper V, the generalization of the yard departure prediction model is explored by applying a simulation-assisted machine learning modeling approach where the model is trained on real-world European yard data and North American simulation yard data. The results show the model has a notable generalized performance with both data types.

Abstract [sv]

Ett av de huvudsakliga målen för att uppnå koldioxidneutralitet i Europa är att öka den modala andelen av godstransporter på järnväg. En av de stora utmaningarna är att övervinna uppfattningen om att godstrafik på järnväg har en låg tillförlitlighet och förutsägbarhet. Gods- och rangerbangårdar har en viktig roll i att tillhandahålla godståg med högre tillförlitlighet och förutsägbarhet. Avvikelser från godstågens planerade avgångstider från godsbangårdar påverkar i förlängningen andra tåg i järnvägsnätet. En högre förutsägbarhet vad gäller godstågens avgångstider från godsbangårdar innebär även en högre förutsägbarhet för tågens körning i nätverket.

Huvudfokus i avhandlingen är att tillämpa datadrivna metoder för att öka förutsägbarheten i godstågens avgångar från godsbangårdar. Deskriptiv analys har använts för att ge en ökad insikt över fördelningen av avgångar från godsbangårdar. Prediktiv analys har tillämpats för att utveckla prediktionsmodeller för avgångar. Slutligen används hybridmodellering för att integrera (koppla ihop) en prediktiv avgångsmodell med andra simuleringsmodeller för större tillämpningar. Doktorsavhandlingen omfattar fem publicerade forskningsartiklar från vilka resultaten presenteras.

I artikel I och II tillämpas deskriptiva analysmetoder för att undersöka sannolikhetsfördelningar för avgångsavvikelser och nätverkets inverkan på avgångsförseningar. Resultaten visar att fördelningar för positiva och negativa avvikelser skiljer sig mellan olika godsbangårdar.  Dessutom påverkar fluktuationer i nätverkets utnyttjandegrad inom kortare tidsperioder avgångsförseningarna. Däremot påvisas ingen korrelation mellan nätverkets påverkan, här definierat som trängsel på ankomstbangården, och avgångsförseningar.

I artikel III tillämpas prediktiv analys genom att utveckla trädbaserade algoritmer för att klassificera status/tillstånden för avgångarna från en godsbangård. Avgångsstatus/avgångstillstånden är obalanserade, en majoritet av tågen är tidiga och en minoritet är försenade. Resultaten visar att prediktionen av försenade avgångar kan förbättras genom att tillämpa metoder för att hantera obalans i data.

De modeller som utvecklats i artikel III utvecklas och utökas vidare i artikel IV och V för att prediktera avgångsavvikelser med en kombinerad modelleringsmetod för två olika tillämpningar. I artikel IV introduceras ett koncept med en maskininlärningsassisterad makrosimuleringsmodell med syftet att integrera avgångsprediktioner från en godsbangård i en makroskopisk nätverkssimuleringsmodell och prediktera godstågens ankomster till nästa godsbangård. Resultaten indikerar en förbättring i prediktionsnoggrannhet jämfört med en grundläggande maskininlärningsmodell och en baslinjemodell för tidtabell.

I artikel V undersöks generaliserbarheten av avgångsprediktionsmodellen genom att tillämpa en ansats med en simuleringsassisterad maskininlärningsmodell och där modellen tränas på verklig data från godsbangårdar i Europa och simuleringsdata från Nordamerika. Resultaten visar att modellens prestanda generellt är god med båda datatyperna.  

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2023. , p. 62
Series
TRITA-ABE-DLT ; 2322
Keywords [en]
Shunting yards, train delays, machine learning, simulation, freight transport
Keywords [sv]
Godsbangårdar, tåg förseningar, maskininlärning, simulering, godstransport
National Category
Transport Systems and Logistics
Research subject
Transport Science, Transport Systems
Identifiers
URN: urn:nbn:se:kth:diva-327021ISBN: 978-91-8040-610-9 (print)OAI: oai:DiVA.org:kth-327021DiVA, id: diva2:1757663
Public defence
2023-06-15, Kollegiesalen, Brinellvägen 8, KTH Campus, video conference link: https://kth-se.zoom.us/j/69650875724, Stockholm, 13:00 (English)
Opponent
Supervisors
Projects
Shift2RailFR8HUBFR8RAIL IIIPRATA
Funder
Swedish Transport Administration
Note

QC 20230522

Available from: 2023-05-22 Created: 2023-05-17 Last updated: 2023-05-29Bibliographically approved
List of papers
1. Statistical Analysis of Departure Deviations from Shunting Yards: Case study from Swedish Railways
Open this publication in new window or tab >>Statistical Analysis of Departure Deviations from Shunting Yards: Case study from Swedish Railways
2021 (English)In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 18Article in journal, Meeting abstract (Refereed) Published
Abstract [en]

Departure deviations from shunting yards impact the reliability of rail freight services and the punctuality of a railway network. Therefore, the statistical analysis of these deviations are necessary for improving the operation of trains in mixed-traffic networks. In our paper, we conduct a detailed statistical analysis of departure deviations considering individual shunting yards characteristics. We use a large freight train delay dataset comprising 250,000 departures over seven years for the two largest shunting yards in Sweden, comparable to other medium-sized shunting yards in Europe. To find the probability distribution of departure deviations, we compare four distribution functions including the exponential, the log-normal, the gamma, and the Weibull according to the maximum likelihood estimates and results of the Anderson-Darling goodness of fit test. In our experiments, we show that the log-normal distribution fits best for delayed departures across both shunting yards, and for early departures at one of them, whereas the gamma distribution fits best for early departures at the other yard. For the temporal delay distribution, we find that fluctuations in the network usage impact the percentage of delayed departures across hours and weekdays, but not across months or years. In addition, we find that freight trains are mostly delayed in the winter.  In the case of hourly delayed departures, we demonstrate that a shunting yard involved with domestic traffic showed a negative correlation between delayed departures and the network usage, whereas an international shunting yard did not, which indicates individuality in shunting yard operations impact shunting yard-network interactions. Our findings mainly contribute to better understanding of departure deviations from shunting yards, thus enhancing the operations and capacity utilization of shunting yards. Moreover, delay distributions can be beneficial in handling delays in traffic management models as well as enhancing the outputs of freight train simulation models

Place, publisher, year, edition, pages
Elsevier BV, 2021
Keywords
Shunting yards, departure deviations, delays, the probability distribution, the temporal delay distribution, exploratory data analysis
National Category
Transport Systems and Logistics
Research subject
Transport Science; Transport Science, Transport Systems; Järnvägsgruppen - Effektiva tågsystem för godstrafik; Järnvägsgruppen - Kapacitet
Identifiers
urn:nbn:se:kth:diva-284669 (URN)10.1016/j.jrtpm.2021.100248 (DOI)000658933800003 ()2-s2.0-85103315788 (Scopus ID)
Projects
Shift2RailFR8HUBFR8RAIL III
Funder
Swedish Transport Administration
Note

QC 20210331

Available from: 2020-11-02 Created: 2020-11-02 Last updated: 2024-03-18Bibliographically approved
2. A departure delay estimation model for freight trains
Open this publication in new window or tab >>A departure delay estimation model for freight trains
2020 (English)In: Proceedings of TRA2020, the 8th Transport Research Arena 2020: Rethinking transport – towards clean and inclusive mobility / [ed] Toni Lusikka, 2020Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

The main objective of this paper is to develop a macroscopic delay estimation model for freight trains departing from the marshalling yard. Freight trains are made up in large marshalling yards comprising three yards (arrival, classification, departure). On time operations in marshaling yards enhances reliability of rail freight services compared to other modes of freight transport. Currently, freight trains encounter most of their delays in marshalling yards even before entering the railway network. Therefore, it is needed to estimate the departure delay of freight trains from the marshaling yard. So far, studies have mainly focused on classification yard operations to estimate departure delay, whereas a proper delay estimation model should be able to consider processes of all three yards. We have developed our model considering main factors (yard congestion, railcar availability and locomotive availability) from all three yards. Hallsberg and Malmö Marshalling yards in Sweden were used as case study.

Series
Traficom Research Reports 7/2020, ISSN 2669-8781
Keywords
Delay Estimation; Marshalling Yards; Machine Learning; Swedish Railways; Shift2Rail
National Category
Transport Systems and Logistics
Research subject
Transport Science; Järnvägsgruppen - Effektiva tågsystem för godstrafik; Transport Science, Transport Systems; Järnvägsgruppen - Kapacitet
Identifiers
urn:nbn:se:kth:diva-284665 (URN)
Conference
the 8th Transport Research Arena 2020:, Helsinki, Finland
Projects
Shift2RailFR8HUB
Funder
Swedish Transport Administration
Note

Part of proceedings: ISBN 978-952-311-484-5

QC 20201124

Available from: 2020-11-02 Created: 2020-11-02 Last updated: 2023-05-17Bibliographically approved
3. The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status
Open this publication in new window or tab >>The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status
2021 (English)In: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, Vol. 2021, article id 3538462Article in journal (Refereed) Published
Abstract [en]

Shunting yards are one of the main areas impacting the reliability of rail freight networks, and delayed departures from shunting yards can further also affect the punctuality of mixed-traffic networks. Methods for automatic detection of departures, which are likely to be delayed, can therefore contribute towards increasing the reliability and punctuality of both freight and passenger services. In this paper, we compare the performance of tree-based methods (decision trees and random forests), which have been highly successful in a wide range of generic applications, in classifying the status of (delayed, early, and on-time) departing trains from shunting yards, focusing on the delayed departures as the minority class. We use a total number of 6,243 train connections (representing over 21,000 individual wagon connections) for a one-month period from the Hallsberg yard in Sweden, which is the largest shunting yard in Scandinavia. Considering our dataset, our results show a slight difference between the application of decision trees and random forests in detecting delayed departures as the minority class. To remedy this, enhanced sampling for minority classes is applied by the synthetic minority oversampling technique (SMOTE) to improve detecting and assigning delayed departures. Applying SMOTE improved the sensitivity, precision, and F-measure of delayed departures by 20% for decision trees and by 30% for random forests. Overall, random forests show a relative better performance in detecting all three departure classes before and after applying SMOTE. Although the preliminary results presented in this paper are encouraging, future studies are needed to investigate the computational performance of tree-based algorithms using larger datasets and considering additional predictors.

Place, publisher, year, edition, pages
Hindawi Limited, 2021
National Category
Transport Systems and Logistics Computer Sciences
Identifiers
urn:nbn:se:kth:diva-303061 (URN)10.1155/2021/3538462 (DOI)000697297200001 ()2-s2.0-85115798799 (Scopus ID)
Note

QC 20211005

Available from: 2021-10-05 Created: 2021-10-05 Last updated: 2023-05-17Bibliographically approved
4. Machine learning-assisted macro simulation for yard arrival prediction
Open this publication in new window or tab >>Machine learning-assisted macro simulation for yard arrival prediction
Show others...
2023 (English)In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 25, article id 100368Article in journal (Refereed) Published
Abstract [en]

Increasing the modal share of the single wagonload transport in Europe requires improving the reliability and predictability of freight trains running between the yards. In this paper, we propose a novel machine learning-assisted macro simulation framework to increase the predictability of yard departures and arrivals. Machine learning is applied through a random forest algorithm to implement a yard departure prediction model. Our yard departure prediction approach is less complex compared to previous yard simulation approaches, and provides an accuracy level of 92% in predictions. Then, departure predictions assist a macro simulation network model (PROTON) to predict arrivals to the succeeding yards. We tested this framework using data from a stretch between two main yards in Sweden; our experiments show that the current framework performs better than the timetable and a basic machine learning arrival prediction model by R2 of 0.48 and a mean absolute error of 35 minutes. Our current results indicate that combination of approaches, including yard and network interactions, can yield competitive results for complex yard arrival time prediction tasks which can assist yard operators and infrastructure managers in yard re-planning processes and yard-network coordination respectively.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Yards, Delay prediction, Macroscopic simulation, Machine learning, Rail traffic
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-324874 (URN)10.1016/j.jrtpm.2022.100368 (DOI)000923576100001 ()2-s2.0-85145972631 (Scopus ID)
Note

QC 20231122

Available from: 2023-03-20 Created: 2023-03-20 Last updated: 2023-11-22Bibliographically approved
5. Application of Simulation-assisted Machine Learning for Yard Departure Prediction
Open this publication in new window or tab >>Application of Simulation-assisted Machine Learning for Yard Departure Prediction
2023 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Increasing the modal share of rail freight is an ongoing goal in Europe and North America. Yards can play an important role in realizing this target by their reliable and predictable performance. We aim at predicting yard departures by implementing a simulation-assisted machine learning model via two general and step-wise concepts for including the predictors. The former adds all predictors at once, and the latter adds them per the availability or the sub-yard. The data used for training the model is a one-year real-world operational data set from a European hump yard and multiple two-year simulation data sets from a representative hump yard in North America. To the best of our knowledge, no previous research has attempted to implement a generalizable prediction model between the European and the North American contexts. The model is developed on a decision tree algorithm based on a 10-fold cross-validation process. Comparing the model performance on three data sets: the real-world, a baseline simulation, and an ultimate randomness simulation shows that the model has a similar performance in the first two data sets with a respective R-squared of 0.90 and 0.87, which shows high capturing of the variance in the data. However, adding large randomness in the simulation decreases the R-squared to 0.70. Results for the step-wise inclusion of the predictors are different for the real-world and simulation data. For the former, adding more operational predictors does not change the model performance, whereas for the latter, adding departure yard predictors increases the R-squared substantially. The global feature importance shows that for the real-world data almost all predictors contribute to a great extent to the predictions, with maximum planned length, departure week day, and the number of arriving trains as the most contributing ones, whereas for the simulation data, the departure yard predictors provide the largest contribution.

Keywords
Yards, machine learning, simulation, delay prediction, rail freight
National Category
Transport Systems and Logistics Computer Sciences
Research subject
Transport Science; Transport Science, Transport Systems; Järnvägsgruppen - Effektiva tågsystem för godstrafik; Järnvägsgruppen - Kapacitet
Identifiers
urn:nbn:se:kth:diva-327017 (URN)
Conference
10th International Conference on Railway Operations Modelling and Analysis
Projects
FR8RAIL IIIPRATAShift2Rail
Funder
Swedish Transport Administration
Note

QC 20230517

Available from: 2023-05-17 Created: 2023-05-17 Last updated: 2023-05-17Bibliographically approved

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