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      A Hierarchical Ensemble Learning Framework for Energy-Efficient Automatic Train Driving

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          Abstract

          Railway transportation plays an important role in modern society. As China’s massive railway transportation network continues to grow in total mileage and operation density, the energy consumption of trains becomes a serious concern. For any given route, the geographic characteristics are known a priori, but the parameters (e.g., loading and marshaling) of trains vary from one trip to another. An extensive analysis of the train operation data suggests that the control gear operation of trains is the most important factor that affects the energy consumption. Such an observation determines that the problem of energy-efficient train driving has to be addressed by considering both the geographic information and the trip parameters. However, the problem is difficult to solve due to its high dimension, nonlinearity, complex constraints, and time-varying characteristics. Faced with these difficulties, we propose an energy-efficient train control framework based on a hierarchical ensemble learning approach. Through hierarchical refinement, we learn prediction models of speed and gear. The learned models can be used to derive optimized driving operations under real-time requirements. This study uses random forest and bagging – REPTree as classification algorithm and regression algorithm, respectively. We conduct an extensive study on the potential of bagging, decision trees, random forest, and feature selection to design an effective hierarchical ensemble learning framework. The proposed framework was testified through simulation. The average energy consumption of the proposed method is over 7% lower than that of human drivers.

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          Author and article information

          Journal
          TST
          Tsinghua Science and Technology
          Tsinghua University Press (Xueyan Building, Tsinghua University, Beijing 100084, China )
          1007-0214
          05 April 2019
          : 24
          : 2
          : 226-237
          Affiliations
          ∙ Guohua Xi is with the CRRC Corporation Limited, Beijing 100078, China.
          ∙ Xibin Zhao, Yan Liu, Jin Huang, and Yangdong Deng are with the School of Software and Key Laboratory for Information System Security, Ministry of Education (KLISS)/Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China.
          Author notes
          * To whom correspondence should be addressed. E-mail: huangjin@ 123456tsinghua.edu.cn .

          Guohua Xi received the BS and ME degrees from Shanghai Railway College in 1985 and 1997, respectively, and got the PhD degree from Central South University in 2007. He is now the President of FAW Group Corporation in China. His research interests include system engineering, rail Transportation, etc.

          Xibin Zhao received the BS, ME, and PhD degrees from Jiangsu University, China, in 1994, 2000, and 2004, respectively. He is currently an associate professor with the School of Software, Tsinghua University, Beijing, China. His research interests include artificial intelligent, reliability analysis of hybrid network systems, and information system security.

          Yan Liu received the BS degree from Xinjiang University, China, in 2014, and ME degree from Tsinghua University, Beijing, China, in 2017. He is now a software engineer at Perfect World Co., Ltd., Beijing, China. His research interests include artificial intelligent, information system security, etc.

          Jin Huang received the PhD and BE degrees from Hunan University, Changsha, China, in 2012 and 2006, respectively. He was also a Joint PhD in Georgia Institute of Technology, USA, during 2009–2011. He started his career as the Postdoc and an assistant professor at the School of Software, Tsinghua University, Beijing, China, since 2013 and 2016, respectively. His research interests include artificial intelligence, cyber-physical systems, automation and mechatronics, fuzzy engineering, etc.

          Yangdong Deng received the ME and BE degrees from Tsinghua University, Beijing, China, in 1998 and 1995, respectively, and the PhD degree in electrical and computer engineering from Carnegie Mellon University, Pittsburgh, PA, USA, in 2006. He is now an associate professor in the School of Software, Tsinghua University, Beijing, China. His current research interests include parallel electronic design automation algorithms, parallel program optimization, general purpose computing on graphics processing hardware, etc.

          Article
          1007-0214-24-2-226
          10.26599/TST.2018.9010114

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