The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status

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Minbashi, Niloofar,1990-KTH Royal Institute of Technology,KTH,Transportplanering,KTH, Transportplanering, Sweden (författare)

The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status

Artikel/kapitelEngelska2021

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Hindawi Limited,2021
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LIBRIS-ID:oai:DiVA.org:kth-303061
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-303061URI
https://doi.org/10.1155/2021/3538462DOI
https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-61257URI
https://lup.lub.lu.se/record/fc8be9cf-0e46-4ec9-9f03-bd5f1b2184d9URI

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QC 20211005
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.

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Bohlin, Markus,1976-KTH Royal Institute of Technology,KTH,Transportplanering,KTH, Transportplanering, Sweden(Swepub:mdh)mbn05 (författare)
Palmqvist, Carl WilliamLund University,Lunds universitet,Trafik och väg,Institutionen för teknik och samhälle,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: Energiomställningen,LTH profilområden,Järnvägsteknik,Forskargrupper vid Lunds universitet,Transport and Roads,Department of Technology and Society,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: The Energy Transition,LTH Profile areas,Faculty of Engineering, LTH,Railway Operation,Lund University Research Groups(Swepub:lu)tft-cpq (författare)
Kordnejad, Behzad,1980-KTH Royal Institute of Technology,KTH,Transportplanering,KTH, Transportplanering, Sweden(Swepub:kth)u1iogp4h (författare)
KTHTransportplanering (creator_code:org_t)

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Ingår i:Journal of Advanced Transportation: Hindawi Limited20210197-67292042-3195

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Av författaren/redakt...: Minbashi, Niloof ...; Bohlin, Markus, ...; Palmqvist, Carl ...; Kordnejad, Behza ...

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