Data-driven train operation models based on data mining and driving experience for the diesel-electric locomotive

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• 作者：Chun-Yang Zhang^a ; ^{zhangcyfst@sina.com" class="auth_mail" title="E-mail the corresponding author} ; Dewang Chen^a ; ^{dwchen@fzu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Jiateng Yin^b ; ^{jiatyin@bjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Long Chen^c ; ^{longchen@umac.mo" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Machine learning ; Ensemble learning ; Data-driven train operation model ; Automatic train operation ; Manual driving
• 刊名：Advanced Engineering Informatics
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：30
• 期：3
• 页码：553-563
• 全文大小：2935 K

文摘

Traditional control methods in automatic train operation (ATO) models have some disadvantages, such as high energy consumption and low riding comfort. To alleviate these shortcomings of the ATO models, this paper presents three data-driven train operation (DTO) models from a new perspective that combines data mining methods with expert knowledge, since the manual driving by experienced drivers can achieve better performance than ATO model in some degree. Based on the experts knowledge that are summarized from experienced train drivers, three DTO models are developed by employing K-nearest neighbor (KNN) and ensemble learning methods, i.e., Bagging-CART (B-CART) and Adaboost.M1-CART (A-CART), into experts systems for train operation. Furthermore, the DTO models are improved via a heuristic train parking algorithm (HPA) to ensure the parking accuracy. With the field data in Chinese Dalian Rapid Rail Line 3 (DRRL3), the effectiveness of the DTO models are evaluated on a simulation platform, and the performance of the proposed DTO models are compared with both ATO and manual driving strategies. The results indicate that the developed DTO models obtain all the merits of the ATO models and the manual driving. That is, they are better than the ATO models in energy consumption and riding comfort, and also outperform the manual driving in stopping accuracy and punctuality. Additionally, the robustness of the proposed model is verified by a number of experiments with some steep gradients and complex speed limits.