Train Performance Analysis Using Heterogeneous Statistical Models

This study investigated the effect of a harsh winter climate on the performance of high-speed passenger trains in northern Sweden. Novel approaches based on heterogeneous statistical models were introduced to analyse the train performance to take time-varying risks of train delays into consideration...

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Bibliographic Details
Main Authors:	Jianfeng Wang, Jun Yu
Format:	Article
Language:	English
Published:	MDPI AG 2021-08-01
Series:	Atmosphere
Subjects:	stratified Cox model heterogeneous Markov chain model likelihood ratio test primary delay arrival delay
Online Access:	https://www.mdpi.com/2073-4433/12/9/1115

id	doaj-8c4f7e8d86ab4a0b84766bac91a36b15
record_format	Article
spelling	doaj-8c4f7e8d86ab4a0b84766bac91a36b152021-09-25T23:43:06ZengMDPI AGAtmosphere2073-44332021-08-01121115111510.3390/atmos12091115Train Performance Analysis Using Heterogeneous Statistical ModelsJianfeng Wang0Jun Yu1Department of Mathematics and Mathematical Statistics, Umeå University, SE 901 87 Umeå, SwedenDepartment of Mathematics and Mathematical Statistics, Umeå University, SE 901 87 Umeå, SwedenThis study investigated the effect of a harsh winter climate on the performance of high-speed passenger trains in northern Sweden. Novel approaches based on heterogeneous statistical models were introduced to analyse the train performance to take time-varying risks of train delays into consideration. Specifically, the stratified Cox model and heterogeneous Markov chain model were used to model primary delays and arrival delays, respectively. Our results showed that weather variables including temperature, humidity, snow depth, and ice/snow precipitation have a significant impact on train performance.https://www.mdpi.com/2073-4433/12/9/1115stratified Cox modelheterogeneous Markov chain modellikelihood ratio testprimary delayarrival delay
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Jianfeng Wang Jun Yu
spellingShingle	Jianfeng Wang Jun Yu Train Performance Analysis Using Heterogeneous Statistical Models Atmosphere stratified Cox model heterogeneous Markov chain model likelihood ratio test primary delay arrival delay
author_facet	Jianfeng Wang Jun Yu
author_sort	Jianfeng Wang
title	Train Performance Analysis Using Heterogeneous Statistical Models
title_short	Train Performance Analysis Using Heterogeneous Statistical Models
title_full	Train Performance Analysis Using Heterogeneous Statistical Models
title_fullStr	Train Performance Analysis Using Heterogeneous Statistical Models
title_full_unstemmed	Train Performance Analysis Using Heterogeneous Statistical Models
title_sort	train performance analysis using heterogeneous statistical models
publisher	MDPI AG
series	Atmosphere
issn	2073-4433
publishDate	2021-08-01
description	This study investigated the effect of a harsh winter climate on the performance of high-speed passenger trains in northern Sweden. Novel approaches based on heterogeneous statistical models were introduced to analyse the train performance to take time-varying risks of train delays into consideration. Specifically, the stratified Cox model and heterogeneous Markov chain model were used to model primary delays and arrival delays, respectively. Our results showed that weather variables including temperature, humidity, snow depth, and ice/snow precipitation have a significant impact on train performance.
topic	stratified Cox model heterogeneous Markov chain model likelihood ratio test primary delay arrival delay
url	https://www.mdpi.com/2073-4433/12/9/1115
work_keys_str_mv	AT jianfengwang trainperformanceanalysisusingheterogeneousstatisticalmodels AT junyu trainperformanceanalysisusingheterogeneousstatisticalmodels
_version_	1717368202377297920

Train Performance Analysis Using Heterogeneous Statistical Models

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