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Tsinghua Science and Technology  2019, Vol. 24 Issue (2): 226-237    doi: 10.26599/TST.2018.9010114
    
A Hierarchical Ensemble Learning Framework for Energy-Efficient Automatic Train Driving
Guohua Xi, Xibin Zhao, Yan Liu, Jin Huang*, Yangdong Deng
∙ 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.
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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.



Key wordsmachine learning      energy efficiency      train driving system      feature selection      ensemble learning     
Received: 20 April 2018      Published: 29 April 2019
Corresponding Authors: Jin Huang   
About author:

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.

Cite this article:

Guohua Xi, Xibin Zhao, Yan Liu, Jin Huang, Yangdong Deng. A Hierarchical Ensemble Learning Framework for Energy-Efficient Automatic Train Driving. Tsinghua Science and Technology, 2019, 24(2): 226-237.

URL:

http://tst.tsinghuajournals.com/10.26599/TST.2018.9010114     OR     http://tst.tsinghuajournals.com/Y2019/V24/I2/226

Fig. 1 Illustration of slope of one railway route.
Fig. 2 A sample of human driving data. Blue and red lines indicate the velocity and driving gears, respectively, for a group of drivers. We draw them together to show that possible patterns exist among the driving operations.
Fig. 3 Proposed solution framework for energy-efficient train driving. REPTree means Reduced Error Pruning Tree.
Fig. 4 First layer for velocity prediction.
Fig. 5 Second layer for gear prediction.
Section typeLableResultant gradient
Steep down grade section-2-3
Gentle down grade section-1-1 to -3
Gentle grade section0-1 to 1
Gentle up grade section11 to 3
Steep up grade section23
Table 1 Route section categories.
Fig. 6 Designed features set for driving records covering train attributes, railway properties, and running information.
ModelDescription
M1Prediction of the velocity change combinations of the slope sections
M2Prediction of the proportion of the velocity in velocity change combinations
M3Prediction of the gear change combinations of the velocity section
M4Prediction of the proportion of the gear in gear change combinations
Table 2 Trained prediction models.
Fig. 7 Power characteristics of selected locomotive for brake.
Fig. 8 Power characteristics of selected locomotive for traction.
Fig. 9 Illustration of railway route using in the experiments.
Fig. 10 Photo of hardware-in-loop test platform of the system with: ①-Working condition generator, ②-LKJ2000 train running monitor and record device, ③-displayer of LKJ2000, ④-supplementary communication device from LKJ2000, ⑤-master controller, ⑥-throttle signal converting device, ⑦-power supplier, ⑧-onboard trip optimization&control device, and ⑨-train motion simulation platform.
ModelPrecision (%)Recall (%)F-Measure (%)CCMAERMSERAE (%)RRSE (%)
M192.492.692.5
M20.99160.01860.04985.196112.9661
M392.092.292.0
M40.98290.01980.06196.606818.4347
Table 3 Evaluation results of trained prediction models.
Fig. 11 Illustration of a sample driving trip by proposed approach compared with a human driver record.
No.Load (ton)EC-Driver (kg)TC-Driver (s)EC-Proposed (kg)TC-Proposed (s)ESTDES (%)
13272229.293876209.463933.519.8357.58.65
23540259.733946241.263980.518.4734.57.11
33574230.543928.5216.483909.514.06–196.10
43603245.273948232.52396012.75125.20
53646246.573941226.554010.520.0269.58.12
63678239.183913221.19393917.99267.52
73702244.573943.5226.353882.518.22–617.45
83997255.623959235.173981.520.4522.58.00
94123259.393997.5240.45400918.9411.57.30
104545283.324257.5266.01419917.31–58.56.11
Avg3768249.353971231.543980.517.819.57.156
Table 4 Comparison of driving performance between drivers and proposed approach.
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