{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T04:40:21Z","timestamp":1760157621190,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004775","name":"Natural Science Foundation of Gansu Province","doi-asserted-by":"crossref","award":["25JRRA1852023-01-01"],"award-info":[{"award-number":["25JRRA1852023-01-01"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Severe snowstorms pose multiple threats to high-speed rail systems, including sudden drops in track friction coefficients, icing of overhead contact lines, and reduced visibility. These conditions can trigger dynamic risks such as train speed restrictions, cascading delays, and operational disruptions. Addressing the limitations of traditional scheduling methods in spatio-temporal modeling during blizzards, real-time multi-objective trade-offs, and high-dimensional constraint solving efficiency, this paper proposes a collaborative optimization approach integrating temporal forecasting with deep reinforcement learning. A dual-module LSTM-PPO model is constructed using LSTM (Long Short-Term Memory) and PPO (Proximal Policy Optimization) algorithms, coupled with a composite reward function. This design collaboratively optimizes punctuality and scheduling stability, enabling efficient schedule adjustments. To validate the proposed method\u2019s effectiveness, a simulation environment based on the Lanzhou-Xinjiang High-Speed Railway line was constructed. Experiments employing a three-stage blizzard evolution mechanism demonstrated that this approach effectively achieves a dynamic equilibrium among safety, punctuality, and scheduling stability during severe snowstorms. This provides crucial decision support for intelligent scheduling of high-speed rail systems under extreme weather conditions.<\/jats:p>","DOI":"10.3390\/systems13100884","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:50:16Z","timestamp":1760107816000},"page":"884","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LSTM-PPO-Based Dynamic Scheduling Optimization for High-Speed Railways Under Blizzard Conditions"],"prefix":"10.3390","volume":"13","author":[{"given":"Na","family":"Wang","sequence":"first","affiliation":[{"name":"School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9989-5008","authenticated-orcid":false,"given":"Zhiyuan","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinzhen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.trb.2006.02.007","article-title":"Stochastic delay propagation in railway networks and phase-type distributions","volume":"41","author":"Meester","year":"2007","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1093\/tse\/tdy001","article-title":"Train delay analysis and prediction based on big data fusion","volume":"1","author":"Wang","year":"2019","journal-title":"Transp. Saf. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.trb.2020.09.001","article-title":"Integrated timetable rescheduling and passenger reassignment during railway disruptions","volume":"140","author":"Zhu","year":"2020","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103025","DOI":"10.1016\/j.trc.2021.103025","article-title":"Integrated optimization of capacitated train rescheduling and passenger reassignment under disruptions","volume":"125","author":"Hong","year":"2021","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.trb.2015.04.001","article-title":"Real-time high-speed train rescheduling in case of a complete blockage","volume":"78","author":"Zhan","year":"2015","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.trb.2006.06.001","article-title":"N-tracked railway traffic re-scheduling during disturbances","volume":"41","author":"Persson","year":"2007","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.omega.2015.11.003","article-title":"Collaborative optimization for train scheduling and train stop planning on high-speed railways","volume":"64","author":"Yang","year":"2016","journal-title":"Omega"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.trc.2015.12.007","article-title":"Optimizing train stopping patterns and schedules for high-speed passenger rail corridors","volume":"63","author":"Yue","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13994","DOI":"10.1109\/TITS.2021.3131202","article-title":"Dynamic scheduling, operation control and their integration in high-speed railways: A review of recent research","volume":"23","author":"Dai","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.cie.2019.02.035","article-title":"A parallel multi-objective genetic algorithm with learning based mutation for railway scheduling","volume":"130","author":"Nitisiri","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.ins.2023.03.003","article-title":"A dynamic rescheduling and speed management approach for high-speed trains with uncertain time-delay","volume":"632","author":"Peng","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107538","DOI":"10.1016\/j.asoc.2021.107538","article-title":"Prediction and analysis of train arrival delay based on XGBoost and Bayesian optimization","volume":"109","author":"Shi","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6307","DOI":"10.1109\/TITS.2023.3253928","article-title":"A multistage decision optimization approach for train timetable rescheduling under uncertain disruptions in a high-speed railway network","volume":"24","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"118834","DOI":"10.1016\/j.eswa.2022.118834","article-title":"Dynamic hybrid mechanism-based differential evolution algorithm and its application","volume":"213","author":"Song","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1080\/01441647.2020.1728419","article-title":"Resilience in railway transport systems: A literature review and research agenda","volume":"40","year":"2020","journal-title":"Transp. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102631","DOI":"10.1016\/j.omega.2022.102631","article-title":"Real-time optimization for train regulation and stop-skipping adjustment strategy of urban rail transit lines","volume":"110","author":"Chen","year":"2022","journal-title":"Omega"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1109\/TCSS.2021.3119944","article-title":"Integration of train control and online rescheduling for high-speed railways in case of emergencies","volume":"9","author":"Dong","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"125964","DOI":"10.1016\/j.physa.2021.125964","article-title":"Discrete-event simulations for metro train operation under emergencies: A multi-agent based model with parallel computing","volume":"573","author":"Li","year":"2021","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103361","DOI":"10.1016\/j.ijdrr.2022.103361","article-title":"Earthquake resilience assessment and improving method of high-speed railway based on train timetable rescheduling","volume":"82","author":"Li","year":"2022","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_20","first-page":"343","article-title":"Simulation-optimization framework for train rescheduling in rapid rail transit","volume":"9","author":"Hassannayebi","year":"2021","journal-title":"Transp. B Transp. Dyn."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"52","DOI":"10.21595\/mrcm.2021.22136","article-title":"Impact of climate change on railway operation and maintenance in Sweden: A State-of-the-art review","volume":"1","author":"Thaduri","year":"2021","journal-title":"Maint. Reliab. Cond. Monit. (MRCM)"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jtrangeo.2017.01.008","article-title":"Weather and rail delays: Analysis of metropolitan rail in Dublin","volume":"59","author":"Brazil","year":"2017","journal-title":"J. Transp. Geogr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4588","DOI":"10.1109\/TIV.2023.3322045","article-title":"ACP-based parallel railway traffic management for high-speed trains in case of emergencies","volume":"8","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1175\/WCAS-D-23-0144.1","article-title":"Impact of extreme rainfall on high-speed rail (HSR) delays in major lines of China","volume":"16","author":"Zhou","year":"2024","journal-title":"Weather Clim. Soc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2344","DOI":"10.1109\/TCST.2022.3140805","article-title":"Hierarchical model predictive control for on-line high-speed railway delay management and train control in a dynamic operations environment","volume":"30","author":"Wang","year":"2022","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13291","DOI":"10.1109\/TITS.2024.3402435","article-title":"Functional Safety and Performance Analysis of Autonomous Route Management for Autonomous Train Control System","volume":"25","author":"Song","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/TIV.2022.3192476","article-title":"Train-centric communication based autonomous train control system","volume":"8","author":"Song","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tranpol.2020.11.008","article-title":"Predicting weather-induced delays of high-speed rail and aviation in China","volume":"101","author":"Chen","year":"2021","journal-title":"Transp. Policy"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1080\/23248378.2022.2094484","article-title":"A multi-output deep learning model based on Bayesian optimization for sequential train delays prediction","volume":"11","author":"Luo","year":"2023","journal-title":"Int. J. Rail Transp."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/MIM.2022.9847199","article-title":"Network delay measurement with machine learning: From lab to real-world deployment","volume":"25","author":"Mohammed","year":"2022","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7500813","DOI":"10.1109\/TIM.2022.3142059","article-title":"Reinforcement learning-based optimal tracking control for levitation system of maglev vehicle with input time delay","volume":"71","author":"Sun","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/TITS.2023.3305074","article-title":"Reinforcement learning for online dispatching policy in real-time train timetable rescheduling","volume":"25","author":"Yue","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"100796","DOI":"10.1016\/j.tbs.2024.100796","article-title":"Energy-saving operation in urban rail transit: A deep reinforcement learning approach with speed optimization","volume":"36","author":"Wang","year":"2024","journal-title":"Travel Behav. Soc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3917","DOI":"10.1109\/TVT.2023.3327762","article-title":"Intelligent beam management based on deep reinforcement learning in high-speed railway scenarios","volume":"73","author":"Qiao","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6336","DOI":"10.1109\/TITS.2023.3248161","article-title":"Hierarchical deep reinforcement learning for self-powered monitoring and communication integrated system in high-speed railway networks","volume":"24","author":"Ling","year":"2023","journal-title":"IEEE Trans. Intell. Transp. 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