{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T14:22:35Z","timestamp":1790000555901,"version":"4.0.1"},"reference-count":54,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,8,16]],"date-time":"2019-08-16T00:00:00Z","timestamp":1565913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The automatic train operation system is a significant component of the intelligent railway transportation. As a fundamental problem, the construction of the train dynamic model has been extensively researched using parametric approaches. The parametric based models may have poor performances due to unrealistic assumptions and changeable environments. In this paper, a long short-term memory network is carefully developed to build the train dynamic model in a nonparametric way. By optimizing the hyperparameters of the proposed model, more accurate outputs can be obtained with the same inputs of the parametric approaches. The proposed model was compared with two parametric methods using actual data. Experimental results suggest that the model performance is better than those of traditional models due to the strong learning ability. By exploring a detailed feature engineering process, the proposed long short-term memory network based algorithm was extended to predict train speed for multiple steps ahead.<\/jats:p>","DOI":"10.3390\/a12080173","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T06:10:14Z","timestamp":1566195014000},"page":"173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction"],"prefix":"10.3390","volume":"12","author":[{"given":"Zhen","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"},{"name":"National Engineering Laboratory for Urban Rail Transit Communication and Operation Control, Beijing 100044, China"},{"name":"Traffic Control Technology Co., Ltd., Beijing 100070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhai","family":"Gao","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Urban Rail Transit Communication and Operation Control, Beijing 100044, China"},{"name":"Traffic Control Technology Co., Ltd., Beijing 100070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1108\/JQME-11-2016-0059","article-title":"Maintenance analytics for railway infrastructure decision support","volume":"23","author":"Famurewa","year":"2017","journal-title":"J. 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