Statistical learning for train delays and influence of winter climate and atmospheric icing
This study investigated the climate effect under consecutive winters on the arrival delay of high-speed passenger trains in northern Sweden. Novel statistical learning approaches, including inhomogeneous Markov chain model and stratified Cox model, were adopted to account for the time-varying risks...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2203.06956 https://arxiv.org/abs/2203.06956 |
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ftdatacite:10.48550/arxiv.2203.06956 2023-05-15T17:44:39+02:00 Statistical learning for train delays and influence of winter climate and atmospheric icing Wang, Jianfeng Nakhai, Roberto Mantas Yu, Jun 2022 https://dx.doi.org/10.48550/arxiv.2203.06956 https://arxiv.org/abs/2203.06956 unknown arXiv Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Applications stat.AP FOS Computer and information sciences Preprint Article article CreativeWork 2022 ftdatacite https://doi.org/10.48550/arxiv.2203.06956 2022-04-01T14:50:31Z This study investigated the climate effect under consecutive winters on the arrival delay of high-speed passenger trains in northern Sweden. Novel statistical learning approaches, including inhomogeneous Markov chain model and stratified Cox model, were adopted to account for the time-varying risks of train delays. The inhomogeneous Markov chain modelling for the arrival delays has used several covariates, including weather variables, train operational direction, and findings from the primary delay analysis through stratified Cox model. The results showed that the weather variables, such as temperature, snow depth, ice/snow precipitation, and train operational direction, significantly impact the arrival delay. The performance of the fitted inhomogeneous Markov chain model was evaluated by the walk-forward validation method. The averaged mean absolute errors between the expected rates and the observed rates of the arrival delay over the train line was obtained at the level of 0.088, which implies that approximately 9% of trains may be misclassified as having arrival delays by the fitted model at a measuring point on the train line. Report Northern Sweden DataCite Metadata Store (German National Library of Science and Technology) |
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Applications stat.AP FOS Computer and information sciences |
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Applications stat.AP FOS Computer and information sciences Wang, Jianfeng Nakhai, Roberto Mantas Yu, Jun Statistical learning for train delays and influence of winter climate and atmospheric icing |
topic_facet |
Applications stat.AP FOS Computer and information sciences |
description |
This study investigated the climate effect under consecutive winters on the arrival delay of high-speed passenger trains in northern Sweden. Novel statistical learning approaches, including inhomogeneous Markov chain model and stratified Cox model, were adopted to account for the time-varying risks of train delays. The inhomogeneous Markov chain modelling for the arrival delays has used several covariates, including weather variables, train operational direction, and findings from the primary delay analysis through stratified Cox model. The results showed that the weather variables, such as temperature, snow depth, ice/snow precipitation, and train operational direction, significantly impact the arrival delay. The performance of the fitted inhomogeneous Markov chain model was evaluated by the walk-forward validation method. The averaged mean absolute errors between the expected rates and the observed rates of the arrival delay over the train line was obtained at the level of 0.088, which implies that approximately 9% of trains may be misclassified as having arrival delays by the fitted model at a measuring point on the train line. |
format |
Report |
author |
Wang, Jianfeng Nakhai, Roberto Mantas Yu, Jun |
author_facet |
Wang, Jianfeng Nakhai, Roberto Mantas Yu, Jun |
author_sort |
Wang, Jianfeng |
title |
Statistical learning for train delays and influence of winter climate and atmospheric icing |
title_short |
Statistical learning for train delays and influence of winter climate and atmospheric icing |
title_full |
Statistical learning for train delays and influence of winter climate and atmospheric icing |
title_fullStr |
Statistical learning for train delays and influence of winter climate and atmospheric icing |
title_full_unstemmed |
Statistical learning for train delays and influence of winter climate and atmospheric icing |
title_sort |
statistical learning for train delays and influence of winter climate and atmospheric icing |
publisher |
arXiv |
publishDate |
2022 |
url |
https://dx.doi.org/10.48550/arxiv.2203.06956 https://arxiv.org/abs/2203.06956 |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.48550/arxiv.2203.06956 |
_version_ |
1766146914117484544 |