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Frontiers of Engineering Management

Front. Eng    2019, Vol. 6 Issue (2) : 139-151
Energy-saving operation approaches for urban rail transit systems
Ziyou GAO, Lixing YANG()
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
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With the accelerated urbanization in China, passenger demand has dramatically increased in large cities, and traffic congestion has become serious in recent years. Developing public urban rail transit systems is an indispensable approach to overcome these problems. However, the high energy consumption of daily operations is an emerging issue due to increased rail transit networks and passenger demands. Thus, reducing the energy consumption and operational cost by using advanced optimization methodologies is an urgent task for operation managers. This work systematically introduces energy-saving approaches for urban rail transit systems in three aspects, namely, train speed profile optimization, utilization of regenerative energy, and integrated optimization of train timetable and speed profile. Future research directions in this field are also proposed to meet increasing passenger demands and network-based urban rail transit systems.

Keywords urban rail transit      energy saving      speed profile optimization      regenerative energy      train timetable     
Corresponding Authors: Lixing YANG   
Just Accepted Date: 29 March 2019   Online First Date: 29 April 2019    Issue Date: 17 May 2019
 Cite this article:   
Ziyou GAO,Lixing YANG. Energy-saving operation approaches for urban rail transit systems[J]. Front. Eng, 2019, 6(2): 139-151.
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Ziyou GAO
Lixing YANG
Energy loss Regenerative energy Total energy
Braking loss Resistance Traction loss
10.9% 17% 30.1% 42% 100%
Tab.1  Composition of traction energy consumption
Fig.1  Recommended and actual speed profiles
Fig.2  GA for optimization of train speed profile
Storage methods Advantages Disadvantages
Battery energy storage Good property in energy saving
High energy density
Expensive equipment cost
Short battery life
Environment pollution
Flywheel energy storage Quick charge
Long life
Complex operation system
High requirement for working environment
Super capacitor energy storage Quick charge and discharge
Long life
Good property in energy saving
Expensive equipment cost
Tab.2  Comparison of three energy storage methods
Fig.3  Schematic of the utilization of regenerative braking energy
Fig.4  Storage and utilization of regenerative braking energy
Fig.5  Integrated optimization of train scheduling and operations
Fig.6  Improving the utilization of regenerative energy by matching the departure times of trains
Fig.7  Speed profiles under different levels
Fig.8  Illustration of the space–time network
1 A L Allegre, A Bouscayrol, P Delarue, P Barrade, E Chattot, S El-Fassi (2010). Energy storage system with supercapacitor for an innovative subway. IEEE Transactions on Industrial Electronics, 57(12): 4001–4012
2 C S Chang, S S Sim (1997). Optimizing train movements through coast control using genetic algorithms. IEE Proceedings-Electric Power Applications, 144(1): 65–73
3 Q Gu, T Tang, F Cao, Y D Song (2014). Energy-efficient train operation in urban rail transit using real-time traffic information. IEEE Transactions on Intelligent Transportation Systems, 15(3): 1216–1233
4 P G Howlett, I P Milroy, P J Pudney (1994). Energy-efficient train control. Control Engineering Practice, 2(2): 193–200
5 Y R Huang, L X Yang, T Tang, Z Y Gao, F Cao, K P Li (2018). Train speed profile optimization with on-board energy storage devices: A dynamic programming based approach. Computers & Industrial Engineering, 126: 149–164
6 K Ichikawa (1968). Application of optimization theory for bounded state variable problems to the operation of train. Bulletin of the JSME, 11(47): 857–865
7 B R Ke, M C Chen, C L Lin (2009). Block-layout design using MAX–MIN ant system for saving energy on mass rapid transit systems. IEEE Transactions on Intelligent Transportation Systems, 10(2): 226–235
8 B R Ke, C L Lin, C C Yang (2012). Optimization of train energy-efficient operation for mass rapid transit systems. IET Intelligent Transport Systems, 6(1): 58–66
9 X Li, H Lo (2014). An energy-efficient scheduling and speed control approach for metro rail operations. Transportation Research Part B: Methodological, 64: 73–89
10 P Liu, L X Yang, Z Y Gao, Y R Huang, S K Li, Y Gao (2018). Energy-efficient train timetable optimization in the subway system with energy storage devices. IEEE Transactions on Intelligent Transportation Systems, 19(12): 3947–3963
11 R Liu, I M Golovitcher (2003). Energy-efficient operation of rail vehicles. Transportation Research Part A: Policy and Practice, 37(10): 917–932
12 C Y Ma, Y Ding, P Du, B H Mao (2010). Study on coast control of train movement for saving energy based-on genetic algorithm. Railway Computer Application, 19(6): 4–8
13 Z Rao (2006). Train Traction Calculation. Beijing: China Railway Press
14 Y Wang (2016). Calculation of Train Operations in Urban Metro Systems. Beijing: Science Press
15 L X Yang, K P Li, Z Y Gao, X Li (2012). Optimizing trains movement on a railway network. Omega, 40(5): 619–633
16 X Yang (2016). Research on train timetable optimization for energy-saving operations in urban rail transit. Dissertation for the Doctoral Degree. Beijing: Beijing Jiaotong University
17 X Yang, X Li, Z Y Gao, H W Wang, T Tang (2013). A cooperative scheduling model for timetable optimization in subway systems. IEEE Transactions on Intelligent Transportation Systems, 14(1): 438–447
18 J T Yin, L X Yang, T Tang, Z Y Gao, B Ran (2017). Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches. Transportation Research Part B: Methodological, 97: 182–213
19 L Zhao (2014). Research on metro timetable optimization model and algorithm based on regenerative braking. Dissertation for the Master Degree. Beijing: Beijing Jiaotong University
20 S Zhao (2014). Research and simulation of urban rail transit super capacitor energy storage system. Dissertation for the Master Degree. Changsha: Central South University of Forestry and Technology
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