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

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Fältnamn	Indikatorer	Metadata
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001		oai:DiVA.org:kth-303061
003		SwePub
008		211005s2021 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
009		oai:DiVA.org:mdh-61257
009		oai:lup.lub.lu.se:fc8be9cf-0e46-4ec9-9f03-bd5f1b2184d9
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3030612 URI
024	7	a https://doi.org/10.1155/2021/35384622 DOI
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-612572 URI
024	7	a https://lup.lub.lu.se/record/fc8be9cf-0e46-4ec9-9f03-bd5f1b2184d92 URI
040		a (SwePub)kthd (SwePub)mdhd (SwePub)lu
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Minbashi, Niloofar,d 1990-u KTH Royal Institute of Technology,KTH,Transportplanering,KTH, Transportplanering, Sweden4 aut0 (Swepub:kth)u1ieou4p
245	1 0	a The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status
264	1	b Hindawi Limited,c 2021
338		a print2 rdacarrier
500		a QC 20211005
520		a 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.
650	7	a TEKNIK OCH TEKNOLOGIERx Samhällsbyggnadsteknikx Transportteknik och logistik0 (SwePub)201052 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Civil Engineeringx Transport Systems and Logistics0 (SwePub)201052 hsv//eng
650	7	a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng
700	1	a Bohlin, Markus,d 1976-u KTH Royal Institute of Technology,KTH,Transportplanering,KTH, Transportplanering, Sweden4 aut0 (Swepub:mdh)mbn05
700	1	a Palmqvist, Carl Williamu Lund 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 Groups4 aut0 (Swepub:lu)tft-cpq
700	1	a Kordnejad, Behzad,d 1980-u KTH Royal Institute of Technology,KTH,Transportplanering,KTH, Transportplanering, Sweden4 aut0 (Swepub:kth)u1iogp4h
710	2	a KTHb Transportplanering4 org
773	0	t Journal of Advanced Transportationd : Hindawi Limitedg 2021q 2021x 0197-6729x 2042-3195
856	4	u https://doi.org/10.1155/2021/3538462y Fulltext
856	4	u https://downloads.hindawi.com/journals/jat/2021/3538462.pdf
856	4	u http://dx.doi.org/10.1155/2021/3538462x freey FULLTEXT
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-303061
856	4 8	u https://doi.org/10.1155/2021/3538462
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-61257
856	4 8	u https://lup.lub.lu.se/record/fc8be9cf-0e46-4ec9-9f03-bd5f1b2184d9

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