Train performance analysis using heterogeneous statistical models

• Published in: Atmosphere
• Main Authors: Wang, Jianfeng, Yu, Jun
• Format: Article in Journal/Newspaper
• Language: English
• Published: Umeå universitet, Institutionen för matematik och matematisk statistik 2021
• Subjects: Stratied Cox model; Heterogeneous Markov chain model; Likelihood ratio test; Primary delay; Arrival delay; Probability Theory and Statistics; Sannolikhetsteori och statistik; Climate Research; Klimatforskning; Transport Systems and Logistics; Transportteknik och logistik; Meteorology and Atmospheric Sciences; Meteorologi och atmosfärforskning; Noice; Northern Sweden
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-186523; https://doi.org/10.3390/atmos12091115

This study investigated the eect of 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 in order to take the time-varying risks of train delays into consideration. Specically, stratied Cox model and heterogeneous Markov chain model were used for modelling primary delays and arrival delays, respectively. Our results showed that the weather variables including temperature, humidity, snow depth, and ice/snow precipitation have signicant impact on the train performance. Special issue, also published as printed edition: Emerging Hydro-Climatic Patterns, Teleconnections and Extreme Events in Changing World at Different Timescales / [ed] Ankit Agarwal, Naiming Yuan, Kevin K.W. Cheung and Roopam Shukla. ISBN 978-3-0365-2953-0 (Hbk); ISBN 978-3-0365-2952-3 (PDF); DOI 10.3390/books978-3-0365-2952-3 NoICE