The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status
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 cont...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/3538462 |
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| _version_ | 1850217028851335168 |
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| author | Niloofar Minbashi Markus Bohlin Carl-William Palmqvist Behzad Kordnejad |
| author_facet | Niloofar Minbashi Markus Bohlin Carl-William Palmqvist Behzad Kordnejad |
| author_sort | Niloofar Minbashi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2f931c4d837d47dbb306af7fb88b3a23 |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-2f931c4d837d47dbb306af7fb88b3a232025-08-20T02:08:09ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/35384623538462The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure StatusNiloofar Minbashi0Markus Bohlin1Carl-William Palmqvist2Behzad Kordnejad3Division of Transport Planning, KTH Royal Institute of Techonology, 100 44 Stockholm, SwedenDivision of Transport Planning, KTH Royal Institute of Techonology, 100 44 Stockholm, SwedenDivision of Transport and Roads, Lund University, P.O. Box 118, 221 00 Lund, SwedenDivision of Transport Planning, KTH Royal Institute of Techonology, 100 44 Stockholm, SwedenShunting 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.http://dx.doi.org/10.1155/2021/3538462 |
| spellingShingle | Niloofar Minbashi Markus Bohlin Carl-William Palmqvist Behzad Kordnejad The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status Journal of Advanced Transportation |
| title | The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status |
| title_full | The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status |
| title_fullStr | The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status |
| title_full_unstemmed | The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status |
| title_short | The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status |
| title_sort | application of tree based algorithms on classifying shunting yard departure status |
| url | http://dx.doi.org/10.1155/2021/3538462 |
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